{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:04:32Z","timestamp":1778753072431,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61703355"],"award-info":[{"award-number":["61703355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11042-020-10474-8","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T18:21:41Z","timestamp":1614450101000},"page":"12061-12075","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Latent representation learning based autoencoder for unsupervised feature selection in hyperspectral imagery"],"prefix":"10.1007","volume":"81","author":[{"given":"Xinxin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongshan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"10474_CR1","unstructured":"Abid A, Balin MF, Zou J (2019) Concrete autoencoders for differentiable feature selection and reconstruction. In: International conference on machine learning, pp 444\u2013453"},{"key":"10474_CR2","unstructured":"Andrychowicz M, Denil M, Gomez S, Hoffman MW, Pfau D, Schaul T, Shillingford B, De Freitas N (2016) Learning to learn by gradient descent by gradient descent. In: Advances in neural information processing systems, pp 3981\u20133989"},{"key":"10474_CR3","unstructured":"Ap S C, Lauly S, Larochelle H, Khapra M, Ravindran B, Raykar V C, Saha A (2014) An autoencoder approach to learning bilingual word representations. In: Advances in neural information processing systems, pp 1853\u20131861"},{"key":"10474_CR4","doi-asserted-by":"crossref","unstructured":"Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 333\u2013342","DOI":"10.1145\/1835804.1835848"},{"key":"10474_CR5","doi-asserted-by":"crossref","unstructured":"Chandra B, Sharma RK (2015) Exploring autoencoders for unsupervised feature selection. In: International joint conference on neural networks, pp 1\u20136","DOI":"10.1109\/IJCNN.2015.7280391"},{"issue":"6","key":"10474_CR6","doi-asserted-by":"publisher","first-page":"2824","DOI":"10.1109\/JSTARS.2015.2441771","volume":"8","author":"M Fauvel","year":"2015","unstructured":"Fauvel M, Dechesne C, Zullo A, Ferraty F (2015) Fast forward feature selection of hyperspectral images for classification with gaussian mixture models. IEEE J Sel Top Appl Earth Observ Remote Sens 8(6):2824\u20132831","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"10474_CR7","doi-asserted-by":"crossref","unstructured":"Feng S, Duarte MF (2018) Graph regularized autoencoder-based unsupervised feature selection. In: Asilomar conference on signals, systems, and computers, pp 55\u201359","DOI":"10.1109\/ACSSC.2018.8645362"},{"key":"10474_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.patcog.2015.08.018","volume":"51","author":"J Feng","year":"2016","unstructured":"Feng J, Jiao L, Liu F, Sun T, Zhang X (2016) Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recognit 51:295\u2013309","journal-title":"Pattern Recognit"},{"issue":"2","key":"10474_CR9","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1007\/s11042-018-6273-1","volume":"78","author":"M Gnouma","year":"2019","unstructured":"Gnouma M, Ladjailia A, Ejbali R, Zaied M (2019) Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimed Tools Appl 78(2):2157\u20132179","journal-title":"Multimed Tools Appl"},{"issue":"7","key":"10474_CR10","first-page":"149","volume":"28","author":"J Gui","year":"2016","unstructured":"Gui J, Sun Z, Ji S, Tao D, Tan T (2016) Feature selection based on structured sparsity: a comprehensive study. IEEE Trans Neural Netw Learn Syst 28(7):149\u20131507","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10474_CR11","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Zhang C, Li C, Xu C (2018) Autoencoder inspired unsupervised feature selection. In: IEEE international conference on acoustics, speech and signal processing, pp 2941\u20132945","DOI":"10.1109\/ICASSP.2018.8462261"},{"key":"10474_CR12","unstructured":"He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Advances in neural information processing systems, pp 507\u2013514"},{"issue":"12","key":"10474_CR13","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TIP.2015.2487860","volume":"24","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659\u20135670","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"10474_CR14","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1007\/s11042-017-5174-z","volume":"78","author":"W Jia","year":"2019","unstructured":"Jia W, Muhammad K, Wang SH, Zhang YD (2019) Five-category classification of pathological brain images based on deep stacked sparse autoencoder. Multimed Tools Appl 78(4):4045\u20134064","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"10474_CR15","doi-asserted-by":"publisher","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","volume":"56","author":"J Jiang","year":"2018","unstructured":"Jiang J, Ma J, Chen C, Wang Z, Cai Z, Wang L (2018) Superpca: a superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery. IEEE Trans Geosci Remote Sens 56(8):4581\u20134593","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10474_CR16","doi-asserted-by":"crossref","unstructured":"Li S, Qi H (2011) Sparse representation based band selection for hyperspectral images. In: IEEE international conference on image processing, pp 2693\u20132696","DOI":"10.1109\/ICIP.2011.6116223"},{"issue":"12","key":"10474_CR17","doi-asserted-by":"publisher","first-page":"5343","DOI":"10.1109\/TIP.2015.2479560","volume":"24","author":"Z Li","year":"2015","unstructured":"Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343\u20135355","journal-title":"IEEE Trans Image Process"},{"key":"10474_CR18","unstructured":"Li J, Hu X, Wu L, Liu H (2009) Robust unsupervised feature selection on networked data. In: SIAM international conference on data mining, pp 387\u2013395"},{"issue":"9","key":"10474_CR19","first-page":"2138","volume":"26","author":"Z Li","year":"2013","unstructured":"Li Z, Liu J, Yang Y, Zhou X, Lu H (2013) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Knowl Data Eng 26(9):2138\u20132150","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"10474_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MSP.2013.2279894","volume":"31","author":"D Lunga","year":"2014","unstructured":"Lunga D, Prasad S, Crawford MM, Ersoy O (2014) Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning. IEEE Signal Process Mag 31(1):55\u201366","journal-title":"IEEE Signal Process Mag"},{"issue":"5","key":"10474_CR21","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/TFUZZ.2019.2936356","volume":"28","author":"J Maillo","year":"2019","unstructured":"Maillo J, Garc\u00eda S, Luengo J, Herrera F, Triguero I (2019) Fast and scalable approaches to accelerate the fuzzy k-nearest neighbors classifier for big data. IEEE Trans Fuzzy Syst 28(5):874\u2013886","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"10474_CR22","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/34.990133","volume":"24","author":"P Mitra","year":"2002","unstructured":"Mitra P, Murthy C, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301\u2013312","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"10474_CR23","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","volume":"48","author":"M Pal","year":"2010","unstructured":"Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by svm. IEEE Trans Geosci Remote Sens 48(5):2297\u20132307","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"10474_CR24","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","volume":"5","author":"S Prasad","year":"2008","unstructured":"Prasad S, Bruce LM (2008) Limitations of principal components analysis for hyperspectral target recognition. IEEE Geosci Remote Sens Lett 5 (4):625\u2013629","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"7","key":"10474_CR25","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1109\/36.934069","volume":"39","author":"SB Serpico","year":"2001","unstructured":"Serpico SB, Bruzzone L (2001) A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 39 (7):1360\u20131367","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"10474_CR26","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/LGRS.2012.2191761","volume":"10","author":"L Shen","year":"2012","unstructured":"Shen L, Zhu Z, Jia S, Zhu J, Sun Y (2012) Discriminative gabor feature selection for hyperspectral image classification. IEEE Geosci Remote Sens Lett 10(1):29\u201333","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10474_CR27","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.neunet.2019.04.015","volume":"117","author":"C Tang","year":"2019","unstructured":"Tang C, Bian M, Liu X, Li M, Zhou H, Wang P, Yin H (2019) Unsupervised feature selection via latent representation learning and manifold regularization. Neural Netw 117:163\u2013178","journal-title":"Neural Netw"},{"issue":"6","key":"10474_CR28","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1109\/TIP.2017.2687128","volume":"26","author":"G Ta\u015fk\u0131n","year":"2017","unstructured":"Ta\u015fk\u0131n G, Kaya H, Bruzzone L (2017) Feature selection based on high dimensional model representation for hyperspectral images. IEEE Trans Image Process 26(6):2918\u20132928","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"10474_CR29","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1109\/TGRS.2019.2958812","volume":"58","author":"Y Wan","year":"2020","unstructured":"Wan Y, Ma A, Zhong Y, Hu X, Zhang L (2020) Multiobjective hyperspectral feature selection based on discrete sine cosine algorithm. IEEE Trans Geosci Remote Sens 58(5):3601\u20133618","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10474_CR30","doi-asserted-by":"crossref","unstructured":"Wang S, Tang J, Liu H (2015) Embedded unsupervised feature selection. In: AAAI conference on artificial intelligence, pp 470\u2013476","DOI":"10.1609\/aaai.v29i1.9211"},{"key":"10474_CR31","doi-asserted-by":"crossref","unstructured":"Wang S, Ding Z, Fu Y (2017) Feature selection guided auto-encoder. In: AAAI conference on artificial intelligence, pp 2725\u20132731","DOI":"10.1609\/aaai.v31i1.10811"},{"key":"10474_CR32","doi-asserted-by":"crossref","unstructured":"Yang Y, Shen H T, Nie F, Ji R, Zhou X (2011) Nonnegative spectral clustering with discriminative regularization. In: Twenty-fifth AAAI conference on artificial intelligence, pp 555\u2013560","DOI":"10.1609\/aaai.v25i1.7922"},{"key":"10474_CR33","doi-asserted-by":"crossref","unstructured":"Yang X, Deng C, Zheng F, Yan J, Liu W (2019) Deep spectral clustering using dual autoencoder network. In: IEEE conference on computer vision and pattern recognition, pp 4066\u20134075","DOI":"10.1109\/CVPR.2019.00419"},{"issue":"1","key":"10474_CR34","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/TCYB.2015.2501373","volume":"47","author":"K Zeng","year":"2015","unstructured":"Zeng K, Yu J, Wang R, Li C, Tao D (2015) Coupled deep autoencoder for single image super-resolution. IEEE Trans Cybern 47(1):27\u201337","journal-title":"IEEE Trans Cybern"},{"issue":"17","key":"10474_CR35","first-page":"1","volume":"11","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Jiang X, Wang X, Cai Z (2019) Spectral-spatial hyperspectral image classification with superpixel pattern and extreme learning machine. Remote Sens 11(17):1\u201320","journal-title":"Remote Sens"},{"key":"10474_CR36","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2019.01.007","volume":"112","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Wu J, Cai Z, Du B, Philip S Y (2019) An unsupervised parameter learning model for rvfl neural network. Neural Netw 112:85\u201397","journal-title":"Neural Netw"},{"key":"10474_CR37","doi-asserted-by":"publisher","unstructured":"Zhang Y, Wu J, Cai Z, Yu P (2020) Multi-view multi-label learning with sparse feature selection for image annotation. IEEE Trans Multimed 1\u201314. https:\/\/doi.org\/10.1109\/TMM.2020.2966887","DOI":"10.1109\/TMM.2020.2966887"},{"issue":"2","key":"10474_CR38","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1109\/TGRS.2014.2333539","volume":"53","author":"Y Zhou","year":"2015","unstructured":"Zhou Y, Peng J, Chen CP (2015) Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(2):1082\u20131095","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"10474_CR39","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TNNLS.2016.2521602","volume":"28","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S, Ju C, Wu X (2016) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 28(6):1263\u20131275","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10474-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-10474-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10474-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:00:28Z","timestamp":1671436828000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-10474-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":39,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10474"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10474-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,27]]},"assertion":[{"value":"15 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}