{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T11:46:49Z","timestamp":1782474409952,"version":"3.54.5"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771080"],"award-info":[{"award-number":["61771080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00521-021-05741-0","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T02:51:15Z","timestamp":1612925475000},"page":"9733-9750","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson\u2019s disease"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7542-4356","authenticated-orcid":false,"given":"Yongming","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"5741_CR1","doi-asserted-by":"crossref","unstructured":"Mirarchi D, Vizza P, Tradigo G et al (2017) Signal analysis for voice evaluation in Parkinson\u2019s disease. In: 2017 IEEE International conference on healthcare informatics (ICHI). IEEE, pp 530\u2013535","DOI":"10.1109\/ICHI.2017.72"},{"issue":"2","key":"5741_CR2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1002\/ana.25514","volume":"86","author":"EJ Vollstedt","year":"2019","unstructured":"Vollstedt EJ, Kasten M, Klein C et al (2019) Using global team science to identify genetic Parkinson\u2019s disease worldwide. Ann Neurol 86(2):153","journal-title":"Ann Neurol"},{"issue":"5","key":"5741_CR3","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","volume":"59","author":"A Tsanas","year":"2012","unstructured":"Tsanas A, Little MA, Mcsharry PE et al (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson\u2019s disease. IEEE Trans Biomed Eng 59(5):1264\u20131271","journal-title":"IEEE Trans Biomed Eng"},{"key":"5741_CR4","doi-asserted-by":"crossref","unstructured":"G\u00fcm\u00fc\u015f\u00e7\u00fc A, Karada\u011f K, Tenekec\u0131 ME et al (2017) Genetic algorithm based feature selection on diagnosis of Parkinson disease via vocal analysis. In: 2017 25th Signal processing and communications applications conference (SIU). IEEE, pp 1\u20134","DOI":"10.1109\/SIU.2017.7960384"},{"key":"5741_CR5","doi-asserted-by":"crossref","unstructured":"Emrani S, McGuirk A, Xiao W (2017) Prognosis and diagnosis of Parkinson's disease using multi-task learning. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1457\u20131466","DOI":"10.1145\/3097983.3098065"},{"issue":"4","key":"5741_CR6","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1109\/TBME.2009.2036000","volume":"57","author":"A Tsanas","year":"2010","unstructured":"Tsanas A, Little MA, Mcsharry PE et al (2010) Accurate telemonitoring of Parkinson\u2019s disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 57(4):884\u2013893","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"5741_CR7","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.jvoice.2017.12.010","volume":"33","author":"P Gillivan-Murphy","year":"2019","unstructured":"Gillivan-Murphy P, Miller N, Carding P (2019) Voice tremor in Parkinson\u2019s disease: an acoustic study. J Voice 33(4):526\u2013535","journal-title":"J Voice"},{"key":"5741_CR8","doi-asserted-by":"crossref","unstructured":"Wroge TJ, \u00d6zkanca Y, Demiroglu C et al (2018) Parkinson\u2019s disease diagnosis using machine learning and voice. In: 2018 IEEE Signal processing in medicine and biology symposium (SPMB). IEEE, pp 1\u20137","DOI":"10.1109\/SPMB.2018.8615607"},{"key":"5741_CR9","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1080\/14737175.2019.1649142","volume":"19","author":"M Magee","year":"2019","unstructured":"Magee M, Copland D, Vogel AP (2019) Motor speech and non-motor language endophenotypes of Parkinson\u2019s disease. Expert Rev Neurother 19:1191\u20131200","journal-title":"Expert Rev Neurother"},{"issue":"6","key":"5741_CR10","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1109\/JBHI.2015.2467375","volume":"19","author":"JR Orozco-Arroyave","year":"2015","unstructured":"Orozco-Arroyave JR, Belalcazar-Bolanos EA, Arias-Londo\u00f1o JD et al (2015) Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases. IEEE J Biomed Health Inform 19(6):1820\u20131828","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"5741_CR11","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.jvoice.2010.01.009","volume":"25","author":"S Skodda","year":"2011","unstructured":"Skodda S, Visser W, Schlegel U (2011) Vowel articulation in Parkinson\u2019s disease. J Voice 25(4):467\u2013472","journal-title":"J Voice"},{"issue":"1","key":"5741_CR12","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1121\/1.4939739","volume":"139","author":"JR Orozco-Arroyave","year":"2016","unstructured":"Orozco-Arroyave JR, H\u00f6nig F, Arias-Londo\u00f1o JD et al (2016) Automatic detection of Parkinson\u2019s disease in running speech spoken in three different languages. J Acoust Soc Am 139(1):481\u2013500","journal-title":"J Acoust Soc Am"},{"issue":"08","key":"5741_CR13","doi-asserted-by":"publisher","first-page":"S183","DOI":"10.1016\/S1353-8020(08)70916-7","volume":"13","author":"J Kalf","year":"2007","unstructured":"Kalf J, De Swart B, Bloem BR et al (2007) 3.414 Guidelines for speech\u2013language therapy in Parkinson\u2019s disease. Parkinsonism Relat Disord 13(08):S183\u2013S184","journal-title":"Parkinsonism Relat Disord"},{"issue":"3","key":"5741_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/24725579.2018.1496495","volume":"8","author":"N Zou","year":"2018","unstructured":"Zou N, Huang X (2018) Empirical Bayes transfer learning for uncertainty characterization in predicting Parkinson\u2019s disease severity. IISE Trans Healthcare Syst Eng 8(3):209\u2013219","journal-title":"IISE Trans Healthcare Syst Eng"},{"issue":"4","key":"5741_CR15","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/JBHI.2013.2245674","volume":"17","author":"BE Sakar","year":"2013","unstructured":"Sakar BE, Isenkul ME, Sakar CO et al (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):828\u2013834","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"5741_CR16","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s00521-019-04069-0","volume":"32","author":"A Naseer","year":"2020","unstructured":"Naseer A, Rani M, Naz S et al (2020) Refining Parkinson\u2019s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839\u2013854","journal-title":"Neural Comput Appl"},{"key":"5741_CR17","doi-asserted-by":"crossref","unstructured":"Al-Fatlawi AH, Jabardi MH, Ling SH (2016) Efficient diagnosis system for Parkinson's disease using deep belief network. In: 2016 IEEE Congress on evolutionary computation (CEC). IEEE, pp 1324\u20131330","DOI":"10.1109\/CEC.2016.7743941"},{"key":"5741_CR18","first-page":"1","volume":"2016","author":"A Derya","year":"2016","unstructured":"Derya A, Akif D (2016) An expert diagnosis system for Parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson\u2019s Dis 2016:1\u20139","journal-title":"Parkinson's Dis"},{"key":"5741_CR19","doi-asserted-by":"publisher","first-page":"17188","DOI":"10.1109\/ACCESS.2017.2741521","volume":"5","author":"Z Cai","year":"2017","unstructured":"Cai Z, Gu J, Chen H et al (2017) A new hybrid intelligent framework for predicting Parkinson\u2019s disease. IEEE Access 5:17188\u201317200","journal-title":"IEEE Access"},{"issue":"4","key":"5741_CR20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/e18040115","volume":"18","author":"H Ozkan","year":"2016","unstructured":"Ozkan H (2016) A comparison of classification methods for telediagnosis of Parkinson\u2019s disease. Entropy 18(4):115","journal-title":"Entropy"},{"issue":"2","key":"5741_CR21","doi-asserted-by":"publisher","first-page":"e88825","DOI":"10.1371\/journal.pone.0088825","volume":"9","author":"S Yang","year":"2014","unstructured":"Yang S, Zheng F, Luo X et al (2014) Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson\u2019s disease. PLoS ONE 9(2):e88825","journal-title":"PLoS ONE"},{"key":"5741_CR22","doi-asserted-by":"crossref","unstructured":"Shahbakhti M, Taherifar D, Sorouri A (2013) Linear and non-linear speech features for detection of Parkinson's disease. In: The 6th 2013 biomedical engineering international conference. IEEE, pp 1\u20133","DOI":"10.1109\/BMEiCon.2013.6687667"},{"issue":"4","key":"5741_CR23","doi-asserted-by":"publisher","first-page":"147","DOI":"10.4236\/jbise.2014.74019","volume":"7","author":"M Shahbakhi","year":"2014","unstructured":"Shahbakhi M, Far DT, Tahami E (2014) Speech analysis for diagnosis of Parkinson\u2019s disease using genetic algorithm and support vector machine. J Biomed Sci Eng 7(4):147\u2013156","journal-title":"J Biomed Sci Eng"},{"issue":"11, supplement","key":"5741_CR24","first-page":"1","volume":"2016","author":"M Behroozi","year":"2016","unstructured":"Behroozi M, Sami A (2016) A multiple-classifier framework for Parkinson\u2019s disease detection based on various vocal tests. Int J Telemed Appl 2016(11, supplement 5):1\u20139","journal-title":"Int J Telemed Appl"},{"key":"5741_CR25","doi-asserted-by":"crossref","unstructured":"V\u00e1squez-Correa JC, Orozco-Arroyave JR, Arora R et al (2017) Multi-view representation learning via GCCA for multimodal analysis of Parkinson's disease. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2966\u20132970","DOI":"10.1109\/ICASSP.2017.7952700"},{"key":"5741_CR26","doi-asserted-by":"crossref","unstructured":"Mekyska J, Rektorova I, Smekal Z (2011) Selection of optimal parameters for automatic analysis of speech disorders in Parkinson's disease. In: 2011 34th International conference on telecommunications and signal processing (TSP). IEEE, pp 408\u2013412","DOI":"10.1109\/TSP.2011.6043700"},{"issue":"4","key":"5741_CR27","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s10916-009-9272-y","volume":"34","author":"CO Sakar","year":"2010","unstructured":"Sakar CO, Kursun O (2010) Telediagnosis of Parkinson\u2019s disease using measurements of dysphonia. J Med Syst 34(4):591\u2013599","journal-title":"J Med Syst"},{"key":"5741_CR28","doi-asserted-by":"crossref","unstructured":"Su M, Chuang KS (2015) Dynamic feature selection for detecting Parkinson's disease through voice signal. In: 2015 IEEE MTT-S 2015 international microwave workshop series on RF and wireless technologies for biomedical and healthcare applications (IMWS-BIO). IEEE, pp 148\u2013149","DOI":"10.1109\/IMWS-BIO.2015.7303822"},{"key":"5741_CR29","doi-asserted-by":"crossref","unstructured":"Caesarendra W, Ariyanto M, Setiawan JD et al (2014) A pattern recognition method for stage classification of Parkinson's disease utilizing voice features. In: 2014 IEEE Conference on biomedical engineering and sciences (IECBES). IEEE, pp 87\u201392","DOI":"10.1109\/IECBES.2014.7047636"},{"key":"5741_CR30","first-page":"4669","volume":"7","author":"E Kaya","year":"2011","unstructured":"Kaya E, Findik O, Babaoglu I et al (2011) Effect of discretization method on the diagnosis of Parkinson\u2019s disease. Int J Innov Comput Inf Control 7:4669\u20134678","journal-title":"Int J Innov Comput Inf Control"},{"key":"5741_CR31","doi-asserted-by":"crossref","unstructured":"Benba A, Jilbab A, Hammouch A (2014) Hybridization of best acoustic cues for detecting persons with Parkinson's disease. In: 2014 Second world conference on complex systems (WCCS). IEEE, pp 622\u2013625","DOI":"10.1109\/ICoCS.2014.7060885"},{"key":"5741_CR32","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.cmpb.2015.12.011","volume":"127","author":"Z Galaz","year":"2016","unstructured":"Galaz Z, Mekyska J, Mzourek Z et al (2016) Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson\u2019s disease. Comput Methods Prog Biomed 127:301\u2013317","journal-title":"Comput Methods Prog Biomed"},{"key":"5741_CR33","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.eswa.2015.10.034","volume":"46","author":"L Naranjo","year":"2016","unstructured":"Naranjo L, P\u00e9rez CJ, Campos-Roca Y et al (2016) Addressing voice recording replications for Parkinson\u2019s disease detection. Expert Syst Appl 46:286\u2013292","journal-title":"Expert Syst Appl"},{"issue":"11","key":"5741_CR34","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10916-015-0353-9","volume":"39","author":"TJ Hirschauer","year":"2015","unstructured":"Hirschauer TJ, Adeli H, Buford JA (2015) Computer-aided diagnosis of Parkinson\u2019s disease using enhanced probabilistic neural network. J Med Syst 39(11):179","journal-title":"J Med Syst"},{"key":"5741_CR35","doi-asserted-by":"crossref","unstructured":"Alqahtani EJ, Alshamrani FH, Syed HF et al (2018) Classification of Parkinson\u2019s disease using NNge classification algorithm. In: 2018 21st Saudi computer society national computer conference (NCC). IEEE, pp 1\u20137","DOI":"10.1109\/NCG.2018.8592989"},{"issue":"10","key":"5741_CR36","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"5741_CR37","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1097\/MNM.0000000000000890","volume":"39","author":"DH Kim","year":"2018","unstructured":"Kim DH, Wit H, Thurston M (2018) Artificial intelligence in the diagnosis of Parkinson\u2019s disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning. Nucl Med Commun 39(10):887\u2013893","journal-title":"Nucl Med Commun"},{"issue":"AUG.","key":"5741_CR38","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.engappai.2018.05.001","volume":"73","author":"D Das","year":"2018","unstructured":"Das D, Lee CSG (2018) Sample-to-sample correspondence for unsupervised domain adaptation. Eng Appl Artif Intell 73(AUG.):80\u201391","journal-title":"Eng Appl Artif Intell"},{"key":"5741_CR39","doi-asserted-by":"crossref","unstructured":"Sun B, Feng J, Saenko K (2016) Return of frustratingly easy domain adaptation. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"5741_CR40","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: European conference on computer vision. Springer, Cham, pp 443\u2013450","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"5741_CR41","doi-asserted-by":"crossref","unstructured":"Sakurai S, Uchiyama H, Shimada A et al (2018) Two-step transfer learning for semantic plant segmentation. In: 7th International conference on pattern recognition applications and methods","DOI":"10.5220\/0006576303320339"},{"key":"5741_CR42","doi-asserted-by":"crossref","unstructured":"An G, Yokota H, Motozawa N et al (2019) Deep learning classification models built with two-step transfer learning for age related macular degeneration diagnosis. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE","DOI":"10.1109\/EMBC.2019.8857468"},{"key":"5741_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12539-020-00393-5","volume":"12","author":"R Zhang","year":"2020","unstructured":"Zhang R, Guo Z, Sun Y et al (2020) COVID19X-rayNet: a two-step transfer learning model for the COVID-19 detecting problem based on a limited number of chest X-ray images. Interdiscip Sci Comput Life Sci 12:1\u201311","journal-title":"Interdiscip Sci Comput Life Sci"},{"issue":"5","key":"5741_CR44","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/TIP.2017.2786469","volume":"27","author":"H Zhang","year":"2018","unstructured":"Zhang H, Patel VM (2018) Convolutional sparse and low-rank coding-based image decomposition. IEEE Trans Image Process 27(5):2121\u20132133","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"5741_CR45","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1109\/TIP.2017.2781303","volume":"27","author":"X Hu","year":"2018","unstructured":"Hu X, Heide F (2018) Convolutional sparse coding for RGB + NIR imaging. IEEE Trans Image Process 27(4):1611\u20131625","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"5741_CR46","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/TIP.2015.2495260","volume":"25","author":"B Wohlberg","year":"2016","unstructured":"Wohlberg B (2016) Efficient algorithms for convolutional sparse representations. IEEE Trans Image Process 25(1):301\u2013315","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"5741_CR47","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TPAMI.2017.2656884","volume":"40","author":"Hang Chang; Ju Han; Cheng Zhong","year":"2018","unstructured":"Hang Chang; Ju Han; Cheng Zhong (2018) Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Trans Pattern Anal Mach Intell 40(5):1182\u20131194","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"5741_CR48","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT et al (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"key":"5741_CR49","unstructured":"Ganin Y, Lempitsky V (2014) Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495"},{"key":"5741_CR50","unstructured":"Bousmalis K, Trigeorgis G, Silberman N et al (2016) Domain separation networks. In: Advances in neural information processing systems, NIPS 2016, pp 343\u2013351"},{"key":"5741_CR51","doi-asserted-by":"crossref","unstructured":"Kang G, Zheng L, Yan Y et al (2018) Deep adversarial attention alignment for unsupervised domain adaptation: the benefit of target expectation maximization. In: Proceedings of the European conference on computer vision (ECCV), pp 401\u2013416","DOI":"10.1007\/978-3-030-01252-6_25"},{"key":"5741_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-58347-1","volume-title":"Domain adaptation in computer vision applications","author":"G Csurka","year":"2017","unstructured":"Csurka G (2017) A comprehensive survey on domain adaptation for visual applications. In: Csurka G (ed) Domain adaptation in computer vision applications. Springer, Cham, pp 1\u201335"},{"key":"5741_CR53","doi-asserted-by":"crossref","unstructured":"Pinheiro PO (2018) Unsupervised domain adaptation with similarity learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8004\u20138013","DOI":"10.1109\/CVPR.2018.00835"},{"key":"5741_CR54","unstructured":"Sener O, Song HO, Saxena A et al (2016) Learning transferrable representations for unsupervised domain adaptation. In: Advances in neural information processing systems, NIPS 2016, pp 2110\u20132118"},{"key":"5741_CR55","doi-asserted-by":"crossref","unstructured":"Haeusser P, Frerix T, Mordvintsev A et al (2017) Associative domain adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2765\u20132773","DOI":"10.1109\/ICCV.2017.301"},{"key":"5741_CR56","unstructured":"Saito K, Ushiku Y, Harada T et al (2017) Adversarial dropout regularization. arXiv preprint arXiv:1711.01575"},{"key":"5741_CR57","doi-asserted-by":"crossref","unstructured":"Saito K, Watanabe K, Ushiku Y et al (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723\u20133732","DOI":"10.1109\/CVPR.2018.00392"},{"key":"5741_CR58","doi-asserted-by":"crossref","unstructured":"Pei Z, Cao Z, Long M et al (2018) Multi-adversarial domain adaptation. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"5741_CR59","doi-asserted-by":"publisher","first-page":"5588","DOI":"10.1109\/TNNLS.2020.2973293","volume":"31","author":"F Liu","year":"2020","unstructured":"Liu F, Zhang G, Lu J (2020) Heterogeneous domain adaptation: an unsupervised approach. IEEE Trans Neural Netw Learn Syst 31:5588\u20135602","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5741_CR60","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G et al (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2200\u20132207","DOI":"10.1109\/ICCV.2013.274"},{"key":"5741_CR61","unstructured":"Gong B, Shi Y, Sha F et al (2015) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE"},{"key":"5741_CR62","doi-asserted-by":"crossref","unstructured":"Wang J, Feng W, Chen Y et al (2018) Visual domain adaptation with manifold embedded distribution alignment. In: 2018 ACM International conference on multimedia, pp 402\u2013410","DOI":"10.1145\/3240508.3240512"},{"key":"5741_CR63","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","volume":"41","author":"M Long","year":"2018","unstructured":"Long M, Cao Y et al (2018) Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell 41:3071\u20133085","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5741_CR64","unstructured":"Long M, Zhu H, Wang J et al (2017) Deep transfer learning with joint adaptation networks. In: The 34th international conference on machine learning, Sydney, pp 2208\u20132217"},{"issue":"1","key":"5741_CR65","first-page":"2096","volume":"17","author":"Y Ganin","year":"2017","unstructured":"Ganin Y, Ustinova E, Ajakan H et al (2017) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096\u20132030","journal-title":"J Mach Learn Res"},{"key":"5741_CR66","unstructured":"Long M, Cao Z, Wang J et al (2018) Conditional adversarial domain adaptation. In: 32nd Conference on neural information processing systems (NeurIPS 2018), Montreal, Canada pp 1640\u20131650"},{"key":"5741_CR67","doi-asserted-by":"crossref","unstructured":"Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, Berlin, pp 171\u2013182","DOI":"10.1007\/3-540-57868-4_57"},{"issue":"1","key":"5741_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"key":"5741_CR69","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Y, Hao S et al (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE International conference on data mining (ICDM). IEEE, pp 1129\u20131134","DOI":"10.1109\/ICDM.2017.150"},{"key":"5741_CR70","unstructured":"Pan SJ, Kwok JT, Yang Q (2008) Transfer learning via dimensionality reduction. In: AAAI, vol 8, pp 677\u2013682"},{"key":"5741_CR71","unstructured":"He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, NIPS 2004, pp 153\u2013160"},{"key":"5741_CR72","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Im: NIPS'01: Proceedings of the 14th international conference on neural information processing systems: natural and synthetic, January 2001, pp 585\u2013591","DOI":"10.7551\/mitpress\/1120.003.0080"},{"issue":"5","key":"5741_CR73","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1162\/089976698300017467","volume":"10","author":"B Sch\u00f6lkopf","year":"1998","unstructured":"Sch\u00f6lkopf B, Smola A, M\u00fcller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299\u20131319","journal-title":"Neural Comput"},{"key":"5741_CR74","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/1475-925X-6-23","volume":"6","author":"MA Little","year":"2007","unstructured":"Little MA, Mcshappy PE, Roberts SJ et al (2007) Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed Eng OnLine 6:23\u201341","journal-title":"BioMed Eng OnLine"},{"issue":"12","key":"5741_CR75","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1007\/s13369-016-2206-3","volume":"41","author":"I Canturk","year":"2016","unstructured":"Canturk I, Karabiber F (2016) A machine learning system for the diagnosis of Parkinson\u2019s disease from speech signals and its application to multiple speech signal types. Arab J Sci Eng 41(12):5049\u20135059","journal-title":"Arab J Sci Eng"},{"key":"5741_CR76","doi-asserted-by":"crossref","unstructured":"Esk\u0131dere \u00d6, Karatutlu A, \u00dcnal C (2015) Detection of Parkinson's disease from vocal features using random subspace classifier ensemble. In: 2015 Twelve international conference on electronics computer and computation (ICECCO). IEEE, pp 1\u20134","DOI":"10.1109\/ICECCO.2015.7416886"},{"issue":"1","key":"5741_CR77","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12938-016-0242-6","volume":"15","author":"H-H Zhang","year":"2016","unstructured":"Zhang H-H, Yang L, Liu Y, Wang P, Yin J, Li Y, Qiu M, Zhu X, Yan F (2016) Classification of Parkinson\u2019s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. BioMed Eng OnLine 15(1):122\u2013143","journal-title":"BioMed Eng OnLine"},{"issue":"6","key":"5741_CR78","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.irbm.2017.10.002","volume":"38","author":"A Benba","year":"2017","unstructured":"Benba A, Jilbab A, Hammouch A (2017) Using human factor cepstral coefficient on multiple types of voice recordings for detecting patients with Parkinson\u2019s disease. IRBM 38(6):346\u2013351","journal-title":"IRBM"},{"key":"5741_CR79","doi-asserted-by":"crossref","unstructured":"Li Y, Zhang C, Jia Y et al (2017) Simultaneous learning of speech feature and segment for classification of Parkinson disease. In: 2017 IEEE 19th International conference on e-health networking, applications and services (Healthcom). IEEE, pp 1\u20136","DOI":"10.1109\/HealthCom.2017.8210820"},{"key":"5741_CR80","doi-asserted-by":"crossref","unstructured":"Vadovsk\u00fd M, Parali\u010d J (2017) Parkinson's disease patients classification based on the speech signals. In: 2017 IEEE 15th International symposium on applied machine intelligence and informatics (SAMI). IEEE, pp 000321\u2013000326","DOI":"10.1109\/SAMI.2017.7880326"},{"key":"5741_CR81","first-page":"1","volume":"2017","author":"YN Zhang","year":"2017","unstructured":"Zhang YN (2017) Can a smartphone diagnose Parkinson disease? A deep neural network method and telediagnosis system implementation. Parkinson\u2019s Dis 2017:1\u201311","journal-title":"Parkinson\u2019s Dis"},{"issue":"3","key":"5741_CR82","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10772-016-9338-4","volume":"19","author":"A Benba","year":"2016","unstructured":"Benba A, Jilbab A, Hammouch A (2016) Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinsons disease and healthy people. Int J Speech Technol 19(3):449\u2013456","journal-title":"Int J Speech Technol"},{"key":"5741_CR83","doi-asserted-by":"crossref","unstructured":"Kraipeerapun P, Amornsamankul S (2015) Using stacked generalization and complementary neural networks to predict Parkinson's disease. In: 2015 11th International conference on natural computation (ICNC). IEEE, pp 1290\u20131294","DOI":"10.1109\/ICNC.2015.7378178"},{"issue":"2","key":"5741_CR84","doi-asserted-by":"publisher","first-page":"e0192192","DOI":"10.1371\/journal.pone.0192192","volume":"13","author":"MM Khan","year":"2018","unstructured":"Khan MM, Mendes A, Chalup SK (2018) Evolutionary wavelet neural network ensembles for breast cancer and Parkinson\u2019s disease prediction. PLoS ONE 13(2):e0192192","journal-title":"PLoS ONE"},{"key":"5741_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2019.2940900","volume":"7","author":"L Ali","year":"2019","unstructured":"Ali L, Zhu C, Zhang Z et al (2019) Automated detection of Parkinson\u2019s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network. IEEE J Transl Eng Health Med 7:1\u201310","journal-title":"IEEE J Transl Eng Health Med"},{"issue":"12","key":"5741_CR86","first-page":"1829","volume":"10","author":"B Shahbaba","year":"2009","unstructured":"Shahbaba B, Neal R (2009) Nonlinear models using Dirichlet process mixtures. J Mach Learn Res 10(12):1829\u20131850","journal-title":"J Mach Learn Res"},{"issue":"10","key":"5741_CR87","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1109\/TNN.2010.2064787","volume":"21","author":"I Psorakis","year":"2010","unstructured":"Psorakis I, Damoulas T, Girolami MA (2010) Multiclass relevance vector machines: sparsity and accuracy. IEEE Trans Neural Netw 21(10):1588\u20131598","journal-title":"IEEE Trans Neural Netw"},{"key":"5741_CR88","unstructured":"Guo PF, Bhattacharya P, Kharma NN (2010) Advances in detecting Parkinson\u2019s disease. In: Medical biometrics, second international conference, ICMB, Hong Kong, China, June. DBLP"},{"issue":"2","key":"5741_CR89","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1016\/j.eswa.2009.06.040","volume":"37","author":"R Das","year":"2010","unstructured":"Das R (2010) A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl 37(2):1568\u20131572","journal-title":"Expert Syst Appl"},{"issue":"3","key":"5741_CR90","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.cmpb.2011.03.018","volume":"104","author":"A Ozcift","year":"2011","unstructured":"Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Prog Biomed 104(3):443\u2013451","journal-title":"Comput Methods Prog Biomed"},{"issue":"4","key":"5741_CR91","doi-asserted-by":"publisher","first-page":"4600","DOI":"10.1016\/j.eswa.2010.09.133","volume":"38","author":"P Luukka","year":"2011","unstructured":"Luukka P (2011) Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 38(4):4600\u20134607","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5741_CR92","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.artmed.2011.02.001","volume":"52","author":"DC Li","year":"2011","unstructured":"Li DC, Liu CW, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif Intell Med 52(1):45\u201352","journal-title":"Artif Intell Med"},{"key":"5741_CR93","doi-asserted-by":"crossref","unstructured":"Spadoto AA, Guido RC, Carnevali FL et al (2011) Improving Parkinson's disease identification through evolutionary-based feature selection. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 7857\u20137860","DOI":"10.1109\/IEMBS.2011.6091936"},{"issue":"4","key":"5741_CR94","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1080\/00207721.2011.581395","volume":"43","author":"K Polat","year":"2012","unstructured":"Polat K (2012) Classification of Parkinson\u2019s disease using feature weighting method on the basis of fuzzy C-means clustering. Int J Syst Sci 43(4):597\u2013609","journal-title":"Int J Syst Sci"},{"issue":"1","key":"5741_CR95","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.eswa.2012.07.014","volume":"40","author":"HL Chen","year":"2013","unstructured":"Chen HL, Huang CC, Yu XG et al (2013) An efficient diagnosis system for detection of Parkinson\u2019s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263\u2013271","journal-title":"Expert Syst Appl"},{"issue":"10","key":"5741_CR96","doi-asserted-by":"publisher","first-page":"12470","DOI":"10.1016\/j.eswa.2011.04.028","volume":"38","author":"F \u00c5str\u00f6m","year":"2011","unstructured":"\u00c5str\u00f6m F, Koker R (2011) A parallel neural network approach to prediction of Parkinson\u2019s disease. Expert Syst Appl 38(10):12470\u201312474","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5741_CR97","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.bspc.2012.04.007","volume":"8","author":"MR Daliri","year":"2013","unstructured":"Daliri MR (2013) Chi-square distance kernel of the gaits for the diagnosis of Parkinson\u2019s disease. Biomed Signal Process Control 8(1):66\u201370","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"5741_CR98","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.bspc.2013.02.006","volume":"8","author":"WL Zuo","year":"2013","unstructured":"Zuo WL, Wang ZY, Liu T et al (2013) Effective detection of Parkinson\u2019s disease using an adaptive fuzzy k-nearest neighbor approach. Biomed Signal Process Control 8(4):364\u2013373","journal-title":"Biomed Signal Process Control"},{"key":"5741_CR99","doi-asserted-by":"crossref","unstructured":"Kadam VJ, Jadhav SM (2019) Feature ensemble learning based on sparse autoencoders for diagnosis of Parkinson\u2019s disease. In: Kadam V, Jadhav SM (eds) Computing, communication and signal processing. Springer, Singapore, pp 567\u2013581","DOI":"10.1007\/978-981-13-1513-8_58"},{"key":"5741_CR100","doi-asserted-by":"crossref","unstructured":"Ma H, Tan T, Zhou H et al (2016) Support vector machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis. In: 2016 Seventh international conference on intelligent control and information processing (ICICIP). IEEE, pp 34\u201340","DOI":"10.1109\/ICICIP.2016.7885912"},{"key":"5741_CR101","doi-asserted-by":"crossref","unstructured":"Dash S, Thulasiram R, Thulasiraman P (2017) An enhanced chaos-based firefly model for Parkinson's disease diagnosis and classification. In: 2017 International conference on information technology (ICIT). IEEE, pp 159\u2013164","DOI":"10.1109\/ICIT.2017.43"},{"issue":"7","key":"5741_CR102","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1007\/s00521-015-2142-2","volume":"28","author":"H G\u00fcr\u00fcler","year":"2017","unstructured":"G\u00fcr\u00fcler H (2017) A novel diagnosis system for Parkinson\u2019s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput Appl 28(7):1657\u20131666","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05741-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05741-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05741-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T06:48:19Z","timestamp":1744181299000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05741-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":102,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["5741"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05741-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,9]]},"assertion":[{"value":"1 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflicts of interest related to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}