{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:13Z","timestamp":1740122653749,"version":"3.37.3"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-06007-7","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T21:14:37Z","timestamp":1733951677000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SparseGraphX: exponentially regularized optimal sparse graph for enhanced label propagation"],"prefix":"10.1007","volume":"55","author":[{"given":"Kanimozhi","family":"M","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4243-9006","authenticated-orcid":false,"given":"Sudhakar","family":"MS","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"6007_CR1","doi-asserted-by":"publisher","unstructured":"Berton L, Lopes ADA (2014) Graph construction based on labeled instances for semi-supervised learning. Proceedings - International Conference on Pattern Recognition 2477\u20132482. https:\/\/doi.org\/10.1109\/ICPR.2014.428","DOI":"10.1109\/ICPR.2014.428"},{"key":"6007_CR2","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/J.NEUCOM.2019.12.130","volume":"408","author":"Y Chong","year":"2020","unstructured":"Chong Y, Ding Y, Yan Q, Pan S (2020) Graph-based semi-supervised learning: a review. Neurocomputing 408:216\u2013230. https:\/\/doi.org\/10.1016\/J.NEUCOM.2019.12.130","journal-title":"Neurocomputing"},{"key":"6007_CR3","doi-asserted-by":"publisher","unstructured":"Deng J, Yu JG (2021) A simple graph-based semi-supervised learning approach for imbalanced classification. Pattern Recognit 118:108026. https:\/\/doi.org\/10.1016\/j.patcog.2021.108026","DOI":"10.1016\/j.patcog.2021.108026"},{"key":"6007_CR4","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TKDE.2019.2901853","volume":"32","author":"F Nie","year":"2020","unstructured":"Nie F, Shi S, Li X (2020) Semi-supervised learning with auto-weighting feature and adaptive graph. IEEE Trans Knowl Data Eng 32:1167\u20131178. https:\/\/doi.org\/10.1109\/TKDE.2019.2901853","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6007_CR5","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1109\/TVCG.2021.3084694","volume":"27","author":"C Chen","year":"2021","unstructured":"Chen C, Wang Z, Wu J et al (2021) Interactive graph construction for graph-based semi-supervised learning. IEEE Trans Vis Comput Graph 27:3701\u20133716. https:\/\/doi.org\/10.1109\/TVCG.2021.3084694","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"11","key":"6007_CR6","doi-asserted-by":"publisher","first-page":"8174","DOI":"10.1109\/TNNLS.2022.3155478","volume":"34","author":"Z Song","year":"2023","unstructured":"Song Z, Yang X, Xu Z, King I (2023) Graph-based semi-supervised learning: a comprehensive review. IEEE Trans Neural Netw Learn Syst 34(11):8174\u20138194. https:\/\/doi.org\/10.1109\/TNNLS.2022.3145691","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR7","unstructured":"Huang J, Nie F, Huang H (2015)\u00a0A new simplex sparse learning model to measure data similarity for clustering. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. pp. 3569\u20133575"},{"key":"6007_CR8","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1007\/S10489-021-02360-Z","volume":"52","author":"Z Hua","year":"2022","unstructured":"Hua Z, Yang Y (2022) Robust and sparse label propagation for graph-based semi-supervised classification. Appl Intell 52:3337\u20133351. https:\/\/doi.org\/10.1007\/S10489-021-02360-Z","journal-title":"Appl Intell"},{"key":"6007_CR9","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s00521-009-0305-8","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xiang S, Liu Y, Zhang C (2010) A general graph-based semi-supervised learning with novel class discovery. Neural Comput Appl 19:549\u2013555. https:\/\/doi.org\/10.1007\/s00521-009-0305-8","journal-title":"Neural Comput Appl"},{"key":"6007_CR10","doi-asserted-by":"publisher","unstructured":"Miquilini P, Rossi RG, Quiles MG et al (2017) Automatically design distance functions for graph-based semi-supervised learning. Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems 933\u2013940. https:\/\/doi.org\/10.1109\/Trustcom\/BigDataSE\/ICESS.2017.333","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.333"},{"key":"6007_CR11","doi-asserted-by":"publisher","unstructured":"Chen Z, Cao H, Chang KCC (2020) GraphEBM: Energy-based graph construction for semi-supervised learning. In: Proceedings - IEEE International Conference on Data Mining, ICDM. Institute of Electrical and Electronics Engineers Inc., pp 62\u201371. https:\/\/doi.org\/10.1109\/ICDM50108.2020.00015","DOI":"10.1109\/ICDM50108.2020.0001"},{"key":"6007_CR12","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.patrec.2020.01.021","volume":"133","author":"J Ma","year":"2020","unstructured":"Ma J, Xiao B, Deng C (2020) Graph based semi-supervised classification with probabilistic nearest neighbors. Pattern Recognit Lett 133:94\u2013101. https:\/\/doi.org\/10.1016\/j.patrec.2020.01.021","journal-title":"Pattern Recognit Lett"},{"key":"6007_CR13","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TNNLS.2020.2984958","volume":"32","author":"F Nie","year":"2021","unstructured":"Nie F, Dong X, Li X (2021) Unsupervised and semisupervised projection with graph optimization. IEEE Trans Neural Netw Learn Syst 32:1547\u20131559. https:\/\/doi.org\/10.1109\/TNNLS.2020.2984958","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR14","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1109\/TKDE.2014.2365793","volume":"27","author":"S Li","year":"2015","unstructured":"Li S, Fu Y (2015) Learning balanced and unbalanced graphs via low-rank coding. IEEE Trans Knowl Data Eng 27:1274\u20131287. https:\/\/doi.org\/10.1109\/TKDE.2014.2365793","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6007_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2015.01.001","volume":"65","author":"Y Peng","year":"2015","unstructured":"Peng Y, Lu BL, Wang S (2015) Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning. Neural Netw 65:1\u201317. https:\/\/doi.org\/10.1016\/j.neunet.2015.01.001","journal-title":"Neural Netw"},{"key":"6007_CR16","doi-asserted-by":"publisher","unstructured":"Nie F, Wang X, Jordan MI, Huang H (2016) The constrained laplacian rank algorithm for graph-based clustering. 30th AAAI Conference on Artificial Intelligence, AAAI 2016 1969\u20131976. https:\/\/doi.org\/10.1609\/aaai.v30i1.10302","DOI":"10.1609\/aaai.v30i1.10302"},{"key":"6007_CR17","doi-asserted-by":"publisher","first-page":"107627","DOI":"10.1016\/j.patcog.2020.107627","volume":"110","author":"Z Kang","year":"2021","unstructured":"Kang Z, Peng C, Cheng Q et al (2021) Structured graph learning for clustering and semi-supervised classification. Pattern Recognit 110:107627. https:\/\/doi.org\/10.1016\/j.patcog.2020.107627","journal-title":"Pattern Recognit"},{"key":"6007_CR18","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xu D, Tsang IWH, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19:1921\u20131932. https:\/\/doi.org\/10.1109\/TIP.2010.2044958","journal-title":"IEEE Trans Image Process"},{"key":"6007_CR19","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.neucom.2014.05.036","volume":"145","author":"A Iosifidis","year":"2014","unstructured":"Iosifidis A, Tefas A, Pitas I (2014) Regularized extreme learning machine for multi-view semi-supervised action recognition. Neurocomputing 145:250\u2013262. https:\/\/doi.org\/10.1016\/j.neucom.2014.05.036","journal-title":"Neurocomputing"},{"key":"6007_CR20","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TIP.2018.2810515","volume":"27","author":"W Wang","year":"2018","unstructured":"Wang W, Yan Y, Nie F et al (2018) Flexible manifold learning with optimal graph for image and video representation. IEEE Trans Image Process 27:2664\u20132675. https:\/\/doi.org\/10.1109\/TIP.2018.2810515","journal-title":"IEEE Trans Image Process"},{"key":"6007_CR21","doi-asserted-by":"publisher","first-page":"2779","DOI":"10.1109\/TNNLS.2018.2886317","volume":"30","author":"Y Pang","year":"2019","unstructured":"Pang Y, Zhou B, Nie F (2019) Simultaneously learning neighborship and projection matrix for supervised dimensionality reduction. IEEE Trans Neural Netw Learn Syst 30:2779\u20132793. https:\/\/doi.org\/10.1109\/TNNLS.2018.2886317","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR22","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1109\/TKDE.2019.2953668","volume":"33","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Chu T, Zhang C (2021) Semi-supervised graph based embedding with non-convex sparse coding techniques. IEEE Trans Knowl Data Eng 33:2193\u20132207. https:\/\/doi.org\/10.1109\/TKDE.2019.2953668","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6007_CR23","doi-asserted-by":"publisher","unstructured":"Yan S, Wang H (2009) Semi-supervised learning by sparse representation. Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 2:788\u2013797. https:\/\/doi.org\/10.1137\/1.9781611972795.68","DOI":"10.1137\/1.9781611972795.68"},{"key":"6007_CR24","doi-asserted-by":"publisher","first-page":"2760","DOI":"10.1109\/TIP.2015.2425545","volume":"24","author":"X Fang","year":"2015","unstructured":"Fang X, Xu Y, Li X et al (2015) Learning a nonnegative sparse graph for linear regression. IEEE Trans Image Process 24:2760\u20132771. https:\/\/doi.org\/10.1109\/TIP.2015.2425545","journal-title":"IEEE Trans Image Process"},{"key":"6007_CR25","doi-asserted-by":"publisher","unstructured":"Madhu C, Sudhakar MS (2024) Dialectic feature-based fuzzy graph learning for label propagation assisting text classification. IEEE Transactions on Fuzzy Systems. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3421595","DOI":"10.1109\/TFUZZ.2024.3421595"},{"key":"6007_CR26","doi-asserted-by":"publisher","unstructured":"Madhu C, Sudhakar MS (2023) An interpretable fuzzy graph learning for label propagation assisting data classification. 1\u201314. https:\/\/doi.org\/10.1109\/TFUZZ.2023.3323093","DOI":"10.1109\/TFUZZ.2023.3323093"},{"key":"6007_CR27","doi-asserted-by":"publisher","unstructured":"Li M, Zhang X, Wang X (2010) An improved learning with local and global consistency. 2010 Chinese Control and Decision Conference, CCDC 2010 1152\u20131156. https:\/\/doi.org\/10.1109\/CCDC.2010.5498148","DOI":"10.1109\/CCDC.2010.5498148"},{"key":"6007_CR28","doi-asserted-by":"publisher","first-page":"107673","DOI":"10.1016\/j.patcog.2020.107673","volume":"111","author":"Z Wang","year":"2021","unstructured":"Wang Z, Nie F, Wang R et al (2021) Local structured feature learning with dynamic maximum entropy graph. Pattern Recognit 111:107673. https:\/\/doi.org\/10.1016\/j.patcog.2020.107673","journal-title":"Pattern Recognit"},{"key":"6007_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01588-5","volume-title":"Graph representation learning","author":"WL Hamilton","year":"2020","unstructured":"Hamilton WL (2020) Graph representation learning. Morgan \\& Claypool Publishers, Cham"},{"key":"6007_CR30","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MSP.2015.2398954","volume":"32","author":"I Dokmanic","year":"2015","unstructured":"Dokmanic I, Parhizkar R, Ranieri J, Vetterli M (2015) Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Process Mag 32:12\u201330","journal-title":"IEEE Signal Process Mag"},{"key":"6007_CR31","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.enfcli.2019.07.121","volume":"30","author":"R Zubaedah","year":"2020","unstructured":"Zubaedah R, Xaverius F, Jayawardana H, Hidayat SH (2020) Comparing euclidean distance and nearest neighbor algorithm in an expert system for diagnosis of diabetes mellitus. Enferm Clin 30:374\u2013377","journal-title":"Enferm Clin"},{"key":"6007_CR32","doi-asserted-by":"publisher","first-page":"2760","DOI":"10.1109\/TIP.2015.2425545","volume":"24","author":"X Fang","year":"2015","unstructured":"Fang X, Xu Y, Li X et al (2015) Learning a nonnegative sparse graph for linear regression. IEEE Trans Image Process 24:2760\u20132771","journal-title":"IEEE Trans Image Process"},{"key":"6007_CR33","doi-asserted-by":"publisher","first-page":"103893","DOI":"10.1016\/j.dsp.2022.103893","volume":"133","author":"M Kanimozhi","year":"2023","unstructured":"Kanimozhi M, Sudhakar MS (2023) A local-global shape characterization scheme using quadratic Bezier triangle aiding retrieval. Digit Signal Process: Rev J 133:103893. https:\/\/doi.org\/10.1016\/j.dsp.2022.103893","journal-title":"Digit Signal Process: Rev J"},{"key":"6007_CR34","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neucom.2015.08.104","volume":"184","author":"Y Wang","year":"2016","unstructured":"Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232\u2013242","journal-title":"Neurocomputing"},{"key":"6007_CR35","doi-asserted-by":"publisher","first-page":"103280","DOI":"10.1016\/j.micpro.2020.103280","volume":"79","author":"M Ramamurthy","year":"2020","unstructured":"Ramamurthy M, Robinson YH, Vimal S, Suresh A (2020) Auto encoder based dimensionality reduction and classification using convolutional neural networks for hyperspectral images. Microprocess Microsyst 79:103280","journal-title":"Microprocess Microsyst"},{"key":"6007_CR36","doi-asserted-by":"publisher","first-page":"102165","DOI":"10.1016\/j.bspc.2020.102165","volume":"63","author":"Y Liu","year":"2021","unstructured":"Liu Y, Li Y, Tan X et al (2021) Local discriminant preservation projection embedded ensemble learning based dimensionality reduction of speech data of Parkinson\u2019s disease. Biomed Signal Process Control 63:102165","journal-title":"Biomed Signal Process Control"},{"key":"6007_CR37","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.future.2021.09.044","volume":"128","author":"T Guo","year":"2022","unstructured":"Guo T, Yu K, Aloqaily M, Wan S (2022) Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT. Futur Gener Comput Syst 128:381\u2013394","journal-title":"Futur Gener Comput Syst"},{"key":"6007_CR38","doi-asserted-by":"publisher","first-page":"3844","DOI":"10.1007\/s10489-020-01986-9","volume":"51","author":"RK Yadav","year":"2021","unstructured":"Yadav RK, Abhishek VS, Venkatesan S (2021) Cross-covariance based affinity for graphs. Appl Intell 51:3844\u20133864","journal-title":"Appl Intell"},{"key":"6007_CR39","doi-asserted-by":"publisher","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","volume":"97","author":"D Singh","year":"2020","unstructured":"Singh D, Singh B (2020) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97:105524. https:\/\/doi.org\/10.1016\/j.asoc.2019.105524","journal-title":"Appl Soft Comput"},{"key":"6007_CR40","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/S0167-8655(00)00112-4","volume":"22","author":"S Aksoy","year":"2001","unstructured":"Aksoy S, Haralick RM (2001) Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recognit Lett 22:563\u2013582. https:\/\/doi.org\/10.1016\/S0167-8655(00)00112-4","journal-title":"Pattern Recognit Lett"},{"key":"6007_CR41","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"Campos GO, Zimek A, Sander J, Campello RJ, Micenkov\u00e1 B, Schubert E, Assent I, Houle ME (2016) On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min Knowl Disc 30:891\u2013927. https:\/\/doi.org\/10.1007\/s10618-015-0444-8","journal-title":"Data Min Knowl Disc"},{"key":"6007_CR42","doi-asserted-by":"publisher","unstructured":"Wang F, Zhu L, Xie L, Zhang Z, Zhong M (2021) Label propagation with structured graph learning for semi-supervised dimension reduction. Knowledge-Based Syst 225:107130.\u00a0https:\/\/doi.org\/10.1016\/j.knosys.2021.107130","DOI":"10.1016\/j.knosys.2021.107130"},{"key":"6007_CR43","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TNNLS.2019.2909737","volume":"31","author":"Y Luo","year":"2019","unstructured":"Luo Y, Wong Y, Kankanhalli M, Zhao Q (2019) G-softmax: improving intraclass compactness and interclass separability of features. IEEE Trans Neural Netw Learn Syst 31:685\u2013699","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR44","doi-asserted-by":"publisher","first-page":"120517","DOI":"10.1016\/j.eswa.2023.120517","volume":"229","author":"Q Li","year":"2023","unstructured":"Li Q (2023) A comprehensive survey of sparse regularization: fundamental, state-of-the-art methodologies and applications on fault diagnosis. Expert Syst Appl 229:120517","journal-title":"Expert Syst Appl"},{"key":"6007_CR45","doi-asserted-by":"publisher","unstructured":"Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with Gabor wavelets. Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 200\u2013205. https:\/\/doi.org\/10.1109\/AFGR.1998.670949","DOI":"10.1109\/AFGR.1998.670949"},{"key":"6007_CR46","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/34.817413","volume":"21","author":"MJ Lyons","year":"1999","unstructured":"Lyons MJ (1999) Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell 21:1357\u20131362. https:\/\/doi.org\/10.1109\/34.817413","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6007_CR47","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1109\/34.927464","volume":"23","author":"AS Georghiades","year":"2001","unstructured":"Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23:643\u2013660. https:\/\/doi.org\/10.1109\/34.927464","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6007_CR48","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16:550\u2013554. https:\/\/doi.org\/10.1109\/34.291440","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6007_CR49","first-page":"223","volume":"95","author":"S Nene","year":"1996","unstructured":"Nene S, Nayar S, Murase H (1996) Columbia object image library (COIL-20). Tech Rep 95:223\u2013303","journal-title":"Tech Rep"},{"key":"6007_CR50","doi-asserted-by":"publisher","first-page":"e0203339","DOI":"10.1371\/journal.pone.0203339","volume":"13","author":"N Ali","year":"2018","unstructured":"Ali N, Zafar B, Riaz F et al (2018) A hybrid geometric spatial image representation for scene classification. PLoS One 13:e0203339","journal-title":"PLoS One"},{"key":"6007_CR51","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1080\/00207160.2013.831082","volume":"91","author":"J Gui","year":"2014","unstructured":"Gui J, Hu R, Zhao Z, Jia W (2014) Semi-supervised learning with local and global consistency. Int J Comput Math 91:2389\u20132402. https:\/\/doi.org\/10.1080\/00207160.2013.831082","journal-title":"Int J Comput Math"},{"key":"6007_CR52","doi-asserted-by":"publisher","unstructured":"Nie F, Wang H, Huang H, Ding C (2011) Unsupervised and semi-supervised learning via \u2113 1-norm graph. Proceedings of the IEEE International Conference on Computer Vision 2268\u20132273. https:\/\/doi.org\/10.1109\/ICCV.2011.6126506","DOI":"10.1109\/ICCV.2011.6126506"},{"key":"6007_CR53","unstructured":"Zhu X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning (ICML-03) (pp. 912\u2013919)"},{"key":"6007_CR54","doi-asserted-by":"publisher","unstructured":"Li CG, Lin Z, Zhang H, Guo J (2015) Learning semi-supervised representation towards a unified optimization framework for semi-supervised learning. Proceedings of the IEEE International Conference on Computer Vision 2015 Inter:2767\u20132775. https:\/\/doi.org\/10.1109\/ICCV.2015.317","DOI":"10.1109\/ICCV.2015.317"},{"key":"6007_CR55","first-page":"912","volume":"2","author":"X Zhu","year":"2003","unstructured":"Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. Proc Twentieth Int Conf Mach Learn 2:912\u2013919","journal-title":"Proc Twentieth Int Conf Mach Learn"},{"key":"6007_CR56","doi-asserted-by":"publisher","unstructured":"Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. 31st AAAI Conference on Artificial Intelligence, AAAI 2017 2408\u20132414. https:\/\/doi.org\/10.1609\/aaai.v31i1.10909","DOI":"10.1609\/aaai.v31i1.10909"},{"key":"6007_CR57","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TNNLS.2020.2984958","volume":"32","author":"F Nie","year":"2020","unstructured":"Nie F, Dong X, Li X (2020) Unsupervised and semisupervised projection with graph optimization. IEEE Trans Neural Netw Learn Syst 32:1547\u20131559","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR58","doi-asserted-by":"publisher","first-page":"107130","DOI":"10.1016\/j.knosys.2021.107130","volume":"225","author":"F Wang","year":"2021","unstructured":"Wang F, Zhu L, Xie L et al (2021) Label propagation with structured graph learning for semi-supervised dimension reduction. Knowl Based Syst 225:107130","journal-title":"Knowl Based Syst"},{"key":"6007_CR59","doi-asserted-by":"publisher","unstructured":"Li Y, Bai L (2023) Label propagation based on bipartite graph. Neural Process Lett 0\u201318. https:\/\/doi.org\/10.1007\/s11063-023-11282-5","DOI":"10.1007\/s11063-023-11282-5"},{"key":"6007_CR60","doi-asserted-by":"publisher","unstructured":"Hua Z, Yang Y (2022) Robust and sparse label propagation for graph-based semi-supervised classification. Applied Intelligence. https:\/\/doi.org\/10.1007\/s10489-022-03666-y","DOI":"10.1007\/s10489-022-03666-y"},{"key":"6007_CR61","doi-asserted-by":"publisher","unstructured":"Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV\u201907) October 2007 Rio de Janeiro. Brazil IEEE 1\u20137. https:\/\/doi.org\/10.1109\/iccv.2007.4408856","DOI":"10.1109\/iccv.2007.4408856"},{"key":"6007_CR62","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.neucom.2017.08.003","volume":"273","author":"S Ren","year":"2018","unstructured":"Ren S, Gu X, Yuan P, Xu H (2018) An iterative paradigm of joint feature extraction and labeling for semi-supervised discriminant analysis. Neurocomputing 273:466\u2013480","journal-title":"Neurocomputing"},{"key":"6007_CR63","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xu D, Tsang IW-H, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19:1921\u20131932","journal-title":"IEEE Trans Image Process"},{"key":"6007_CR64","doi-asserted-by":"publisher","first-page":"107425","DOI":"10.1016\/j.patcog.2020.107425","volume":"107","author":"R Zhu","year":"2020","unstructured":"Zhu R, Dornaika F, Ruichek Y (2020) Semi-supervised elastic manifold embedding with deep learning architecture. Pattern Recognit 107:107425","journal-title":"Pattern Recognit"},{"key":"6007_CR65","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s13042-022-01701-9","volume":"14","author":"F Dornaika","year":"2023","unstructured":"Dornaika F, Hoang VT (2023) Deep data representation with feature propagation for semi-supervised learning. Int J Mach Learn Cybern 14:1303\u20131316","journal-title":"Int J Mach Learn Cybern"},{"key":"6007_CR66","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neunet.2018.12.008","volume":"111","author":"R Zhu","year":"2019","unstructured":"Zhu R, Dornaika F, Ruichek Y (2019) Learning a discriminant graph-based embedding with feature selection for image categorization. Neural Netw 111:35\u201346","journal-title":"Neural Netw"},{"key":"6007_CR67","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/TNNLS.2011.2178037","volume":"23","author":"Y Huang","year":"2012","unstructured":"Huang Y, Xu D, Nie F (2012) Semi-supervised dimension reduction using trace ratio criterion. IEEE Trans Neural Netw Learn Syst 23:519\u2013526. https:\/\/doi.org\/10.1109\/TNNLS.2011.2178037","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6007_CR68","doi-asserted-by":"publisher","unstructured":"Wang D, Nie F, Huang H (2014) Large-scale adaptive semi-supervised learning via unified inductive and transductive model. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 482\u2013491. https:\/\/doi.org\/10.1145\/2623330.2623731","DOI":"10.1145\/2623330.2623731"},{"key":"6007_CR69","doi-asserted-by":"publisher","unstructured":"Zhou Y, Sun S (2015) Semisupervised tangent space discriminant analysis. Math Probl Eng 2015. https:\/\/doi.org\/10.1155\/2015\/706180","DOI":"10.1155\/2015\/706180"},{"key":"6007_CR70","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.24963\/ijcai.2017\/321","volume":"0","author":"H Liu","year":"2017","unstructured":"Liu H, Han J, Nie F (2017) Semi-supervised orthogonal graph embedding with recursive projections. IJCAI Int Joint Conf Artif Intell 0:2308\u20132314. https:\/\/doi.org\/10.24963\/ijcai.2017\/321","journal-title":"IJCAI Int Joint Conf Artif Intell"},{"key":"6007_CR71","doi-asserted-by":"publisher","first-page":"4609","DOI":"10.1109\/TKDE.2021.3049371","volume":"34","author":"F Nie","year":"2021","unstructured":"Nie F, Wang Z, Wang R, Li X (2021) Adaptive local embedding learning for semi-supervised dimensionality reduction. IEEE Trans Knowl Data Eng 34:4609\u20134621. https:\/\/doi.org\/10.1109\/TKDE.2021.3049371","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"6007_CR72","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06007-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06007-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06007-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:05:11Z","timestamp":1737385511000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06007-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6007"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06007-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,12,11]]},"assertion":[{"value":"30 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"131"}}