{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T09:03:55Z","timestamp":1777626235378,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the National Program on Key Research Project of China","award":["2019YFE0103900"],"award-info":[{"award-number":["2019YFE0103900"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["861917-SAFFI"],"award-info":[{"award-number":["861917-SAFFI"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32071481"],"award-info":[{"award-number":["32071481"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07409-9","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T16:03:00Z","timestamp":1655481780000},"page":"18407-18420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Subspace embedding for classification"],"prefix":"10.1007","volume":"34","author":[{"given":"Zheng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Mu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"7409_CR1","first-page":"2507","volume":"19","author":"S Yvan","year":"2007","unstructured":"Yvan S, Iaki I, Pedro L (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 19:2507\u20132517","journal-title":"Bioinformatics"},{"issue":"3","key":"7409_CR2","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TCDS.2017.2766258","volume":"10","author":"W Li","year":"2017","unstructured":"Li W, Duan F, Sheng S, Xu C, Liu R, Zhang Z, Jiang X (2017) A human-vehicle collaborative simulated driving system based on hybrid brain-computer interfaces and computer vision. IEEE Trans Cognit Develop Syst 10(3):810\u2013822","journal-title":"IEEE Trans Cognit Develop Syst"},{"key":"7409_CR3","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ijrefrig.2016.10.014","volume":"74","author":"JL Xu","year":"2017","unstructured":"Xu JL, Sun DW (2017) Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm. Int J Refrig 74:151\u2013164","journal-title":"Int J Refrig"},{"key":"7409_CR4","doi-asserted-by":"crossref","unstructured":"G. Ren, J. Ning, Z. Zhang, Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality. Spectrochimica Acta A Mol Biomol Spectrosc 118918","DOI":"10.1016\/j.saa.2020.118918"},{"issue":"12","key":"7409_CR5","first-page":"3809","volume":"39","author":"LI Yu-Qiang","year":"2019","unstructured":"Yu-Qiang LI, Pan TH, Hao-Ran LI, Zou XB (2019) Nir spectral feature selection using lasso method and its application in the classification analysis. Spectroscopy Spectral Anal 39(12):3809\u20133815","journal-title":"Spectroscopy Spectral Anal"},{"issue":"1\u20133","key":"7409_CR6","first-page":"324","volume":"73","author":"L Jing","year":"2009","unstructured":"Jing L, Allinson NM (2009) Subspace learning-based dimensionality reduction in building recognition. Neurocomputing 73(1\u20133):324\u2013330","journal-title":"Neurocomputing"},{"issue":"6","key":"7409_CR7","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1109\/TMM.2016.2646219","volume":"19","author":"Z Liang","year":"2017","unstructured":"Liang Z, Ma B, Li G, Huang Q, Qi T (2017) Cross-modal retrieval using multi-ordered discriminative structured subspace learning. IEEE Trans Multim 19(6):1220\u20131233","journal-title":"IEEE Trans Multim"},{"key":"7409_CR8","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neunet.2018.07.017","volume":"108","author":"Z Zhao","year":"2018","unstructured":"Zhao Z, Lei J, Zhao M, Ye Q, Min Z, Meng W (2018) Adaptive non-negative projective semi-supervised learning for inductive classification. Neural Netw 108:128\u2013145","journal-title":"Neural Netw"},{"issue":"4","key":"7409_CR9","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ (2010) Principal component analysis, Wiley Interdisciplinary Reviews. Comput Stat 2(4):433\u2013459","journal-title":"Comput Stat"},{"key":"7409_CR10","doi-asserted-by":"crossref","unstructured":"Deepa P, Thilagavathi K (2015) Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), pp 656\u2013660","DOI":"10.1109\/ECS.2015.7124989"},{"key":"7409_CR11","doi-asserted-by":"crossref","unstructured":"Tang G, Lu G, Wang Z, Xie Y,(2016) Robust tensor principal component analysis by lp-norm for image analysis. In: 2016 2nd IEEE international conference on computer and communications (ICCC), pp 568\u2013573","DOI":"10.1109\/CompComm.2016.7924765"},{"key":"7409_CR12","first-page":"429","volume":"2018","author":"B Li","year":"2018","unstructured":"Li B (2018) A principal component analysis approach to noise removal for speech denoising, in. International Conference on Virtual Reality and Intelligent Systems (ICVRIS) 2018:429\u2013432","journal-title":"International Conference on Virtual Reality and Intelligent Systems (ICVRIS)"},{"issue":"9","key":"7409_CR13","doi-asserted-by":"publisher","first-page":"6718","DOI":"10.1016\/j.eswa.2010.02.107","volume":"37","author":"X Yong","year":"2010","unstructured":"Yong X, Song F, Ge F, Zhao Y (2010) A novel local preserving projection scheme for use with face recognition. Expert Syst Appl 37(9):6718\u20136721","journal-title":"Expert Syst Appl"},{"issue":"5500","key":"7409_CR14","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"S Roweis","year":"2000","unstructured":"Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"7","key":"7409_CR15","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1007\/s13042-018-0849-y","volume":"10","author":"W Zhang","year":"2019","unstructured":"Zhang W, Kang P, Fang X, Teng L, Han N (2019) Joint sparse representation and locality preserving projection for feature extraction. Int J Mach Learn Cybern 10(7):1731\u20131745","journal-title":"Int J Mach Learn Cybern"},{"key":"7409_CR16","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2013.08.040","volume":"128","author":"X Fang","year":"2014","unstructured":"Fang X, Yong X, Li X, Fan Z, Hong L, Yan C (2014) Locality and similarity preserving embedding for feature selection. Neurocomputing 128:304\u2013315","journal-title":"Neurocomputing"},{"issue":"1","key":"7409_CR17","first-page":"48","volume":"39","author":"Z Qiao","year":"2009","unstructured":"Qiao Z, Zhou L, Huang J (2009) Sparse linear discriminant analysis with applications to high dimensional low sample size data. IAENG Int J Appl Math 39(1):48\u201360","journal-title":"IAENG Int J Appl Math"},{"key":"7409_CR18","doi-asserted-by":"crossref","unstructured":"Sheng W, Lu J, Gu X, Du H, Yang J,(2016) Semi-supervised linear discriminant analysis for dimension reduction and classification. Pattern Recognit 57(C):179\u2013189","DOI":"10.1016\/j.patcog.2016.02.019"},{"issue":"11","key":"7409_CR19","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.1109\/TNNLS.2015.2391201","volume":"26","author":"D Chu","year":"2017","unstructured":"Chu D, Liao LZ, Ng KP, Wang X (2017) Incremental linear discriminant analysis: a fast algorithm and comparisons. IEEE Trans Neural Netw Learn Syst 26(11):2716\u20132735","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"7409_CR20","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/LSP.2018.2869107","volume":"25","author":"H Liang","year":"2018","unstructured":"Liang H, Chen X, Xu C, Jia L, Johnson MT (2018) Local pairwise linear discriminant analysis for speaker verification. IEEE Signal Process Lett 25(10):1575\u20131579","journal-title":"IEEE Signal Process Lett"},{"key":"7409_CR21","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neucom.2014.12.057","volume":"156","author":"X Shu","year":"2015","unstructured":"Shu X, Xu H, Tao L (2015) A least squares formulation of multi-label linear discriminant analysis. Neurocomputing 156:221\u2013230","journal-title":"Neurocomputing"},{"issue":"3","key":"7409_CR22","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1109\/TIFS.2013.2243146","volume":"8","author":"J Lu","year":"2013","unstructured":"Lu J, Tan YP (2013) Cost-sensitive subspace analysis and extensions for face recognition. IEEE Trans Inf Forensics Secur 8(3):510\u2013519","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"7409_CR23","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TCSVT.2018.2799214","volume":"29","author":"J Wen","year":"2019","unstructured":"Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2019) Robust sparse linear discriminant analysis. IEEE Trans Circuits Syst Video Technol 29(2):392\u2013403","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"7409_CR24","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","volume":"29","author":"S Yan","year":"2007","unstructured":"Yan S, Xu D, Zhang B (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40\u201351","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"7409_CR25","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/TNN.2005.860852","volume":"17","author":"H Li","year":"2006","unstructured":"Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157\u2013165","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"7409_CR26","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1109\/TSMCB.2012.2218234","volume":"43","author":"F Dornaika","year":"2013","unstructured":"Dornaika F, Bosaghzadeh A (2013) Exponential local discriminant embedding and its application to face recognition. IEEE Trans Cybern 43(3):921\u2013934","journal-title":"IEEE Trans Cybern"},{"issue":"11","key":"7409_CR27","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1016\/j.patcog.2009.04.015","volume":"42","author":"F Wang","year":"2009","unstructured":"Wang F, Xin W, Zhang D, Zhang C, Tao L (2009) marginface: a novel face recognition method by average neighborhood margin maximization. Pattern Recognit 42(11):2863\u20132875","journal-title":"Pattern Recognit"},{"key":"7409_CR28","doi-asserted-by":"crossref","unstructured":"Masoudimansour W, Bouguila N (2020) Supervised dimensionality reduction of proportional data using mixture estimation. Pattern Recognit 105:107379","DOI":"10.1016\/j.patcog.2020.107379"},{"issue":"6","key":"7409_CR29","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1109\/TSMCB.2012.2196995","volume":"42","author":"X Jiang","year":"2012","unstructured":"Jiang X, Gao J, Wang T, Zheng L (2012) Supervised latent linear gaussian process latent variable model for dimensionality reduction, IEEE Transactions on Systems, Man, and Cybernetics. Part B (Cybernetics) 42(6):1620\u20131632","journal-title":"Part B (Cybernetics)"},{"key":"7409_CR30","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.neucom.2016.09.048","volume":"237","author":"KR Murthy","year":"2017","unstructured":"Murthy KR, Ghosh A (2017) Moments discriminant analysis for supervised dimensionality reduction. Neurocomputing 237:114\u2013132","journal-title":"Neurocomputing"},{"key":"7409_CR31","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.neucom.2014.02.017","volume":"139","author":"K Than","year":"2014","unstructured":"Than K, Tu BH, Nguyen DK (2014) An effiective framework for supervised dimension reduction. Neurocomputing 139:397\u2013407","journal-title":"Neurocomputing"},{"issue":"1","key":"7409_CR32","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7(1):2399\u20132434","journal-title":"J Mach Learn Res"},{"key":"7409_CR33","unstructured":"Deng C, He X, Han J (2007) Semi-supervised discriminant analysis. In: 2007 11th IEEE international conference on computer vision, pp 1\u20137"},{"key":"7409_CR34","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.patcog.2016.07.029","volume":"61","author":"F Dornaika","year":"2017","unstructured":"Dornaika F, ElTraboulsia Y (2017) Matrix exponential based semi-supervised discriminant embedding for image classification. Pattern Recognit 61:92\u2013103","journal-title":"Pattern Recognit"},{"key":"7409_CR35","doi-asserted-by":"publisher","first-page":"377","DOI":"10.5194\/isprsannals-I-3-377-2012","volume":"1\u20133","author":"H Huang","year":"2012","unstructured":"Huang H, Liu J, Pan Y (2012) Semi-supervised marginal lisher analysis for hyperspectral image classification. ISPRS Ann Photogramm Remote Sens Spatial Inform Sci 1\u20133:377\u2013382","journal-title":"ISPRS Ann Photogramm Remote Sens Spatial Inform Sci"},{"issue":"6","key":"7409_CR36","doi-asserted-by":"publisher","first-page":"2502","DOI":"10.1109\/TNNLS.2017.2693221","volume":"29","author":"X Fang","year":"2018","unstructured":"Fang X, Teng S, Lai Z et al (2018) Robust latent subspace learning for image classification. IEEE Trans Neural Netw Learn Syst 29(6):2502\u20132515","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7409_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2017.01.001","volume":"88","author":"X Fang","year":"2017","unstructured":"Fang X, Xu Y, Li X et al (2017) Orthogonal self-guided similarity preserving projection for classification and clustering. Neural Netw 88:1\u20138","journal-title":"Neural Netw"},{"issue":"2","key":"7409_CR38","doi-asserted-by":"publisher","first-page":"97","DOI":"10.2478\/fcds-2013-0002","volume":"38","author":"K Jedrzejewski","year":"2013","unstructured":"Jedrzejewski K, Zamorski M (2013) Performance of k-nearest neighbors algorithm in opinion classification. Found Comput Decis Sci 38(2):97\u2013110","journal-title":"Found Comput Decis Sci"},{"key":"7409_CR39","doi-asserted-by":"crossref","unstructured":"Liu G, Yan S (2011) Latent low-rank representation for subspace segmentation and feature extraction. In: International conference on computer vision, pp 1615\u20131622","DOI":"10.1109\/ICCV.2011.6126422"},{"key":"7409_CR40","doi-asserted-by":"crossref","unstructured":"Ren LR, Gao YL, Liu JX, Zhu R, Kong XZ (2020) $${l_{2,1}}$$-extreme learning machine: an efficient robust classifier for tumor classification. Comput Biol Chem 89:107368","DOI":"10.1016\/j.compbiolchem.2020.107368"},{"key":"7409_CR41","unstructured":"Flach P, Kull M (2015) Precision-recall-gain curves: PR analysis done right. In: Proceedings of the 28th international conference on neural information processing systems, pp 838\u2013846"},{"issue":"1","key":"7409_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37\u201346","journal-title":"Educ Psychol Measur"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07409-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07409-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07409-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T21:23:42Z","timestamp":1666301022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07409-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":42,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7409"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07409-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]},"assertion":[{"value":"9 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}