{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:29:35Z","timestamp":1743028175589,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031824746"},{"type":"electronic","value":"9783031824753"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-82475-3_13","type":"book-chapter","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T14:34:27Z","timestamp":1741012467000},"page":"192-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Concept Factorization with Manifold and Orthogonal Constraint for Data Representation"],"prefix":"10.1007","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengcai","family":"Leng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Lu, G.F., Wang, Y., Zou, J.: Low-rank matrix factorization with adaptive graph regularizer. IEEE Trans. Image Process. 25(5), 2196\u20132205 (2016)","DOI":"10.1109\/TIP.2016.2542919"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Zhu, W., Yang, S., Zhu, Y.: Restricted connection orthogonal matching pursuit for sparse subspace clustering. IEEE Signal Process. Lett. 26(12), 1892\u20131896 (2019)","DOI":"10.1109\/LSP.2019.2953638"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Zhao, L., Chen, Z., Yang, Y., Zou, L., Wang, Z.J.: ICFS clustering with multiple representatives for large data. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 728\u2013738 (2019)","DOI":"10.1109\/TNNLS.2018.2851979"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Su, Y., Hong, D., Li, Y., Jing, P.: Low-rank regularized deep collaborative matrix factorization for micro-video multi-label classification. IEEE Signal Process. Lett. 27, 740\u2013744 (2020)","DOI":"10.1109\/LSP.2020.2983831"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, J., Yu, Y., Wang, Z., Liu, X., Ma, J.: Graph-regularized locality-constrained joint dictionary and residual learning for face sketch synthesis. IEEE Trans. Image Process. 28(2), 628\u2013641 (2019)","DOI":"10.1109\/TIP.2018.2870936"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhong, Y., Zhang, L., Xu, Y.: Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 55(11), 6287\u20136304 (2017)","DOI":"10.1109\/TGRS.2017.2724944"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Mei, X., Ma, Y., Li, C., Fan, F., Huang, J., Ma, J.: Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation. Neurocomputing 275, 2783\u20132797 (2018)","DOI":"10.1016\/j.neucom.2017.11.052"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhao, K.: Low-rank matrix approximation with manifold regularization. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1717\u20131729 (2013)","DOI":"10.1109\/TPAMI.2012.274"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000)","DOI":"10.1126\/science.290.5500.2319"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323\u20132326 (2000)","DOI":"10.1126\/science.290.5500.2323"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, vol. 14, pp. 585\u2013591(2001)","DOI":"10.7551\/mitpress\/1120.003.0080"},{"key":"13_CR12","unstructured":"Cai, D., He, X., Han, J..: Isometric projection. In: Proceeding of the National Conference on Artificial Intelligence, Vancouver, BC, Canada, vol. 1, pp. 528\u2013533 (2007)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Najafi, A., Joudaki, A., Fatemizadeh, E.: Nonlinear dimensionality reduction via path-based isometric mapping. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1452\u20131464 (2016)","DOI":"10.1109\/TPAMI.2015.2487981"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Xu, W., Liu, X., Gong, YH.: Document clustering based on non-negative matrix factorization. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR\u201903), pp. 267\u2013273 (2003)","DOI":"10.1145\/860435.860485"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Xu, W., Gong, Y.H.: Document clustering by concept factorization. In: Proceedings of the 27th annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR\u201904), Sheffield, UK, pp. 202\u2013209 (2004)","DOI":"10.1145\/1008992.1009029"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788\u2013791 (1999)","DOI":"10.1038\/44565"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, L., Chen, Z., Wang, Z.J.: Unsupervised multiview nonnegative correlated feature learning for data clustering. IEEE Signal Process. Lett. 25(1), 60\u201364 (2018)","DOI":"10.1109\/LSP.2017.2769086"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Leng, C., Cai, G., Yu, D., Wang, Z.: Adaptive total-variation for non-negative matrix factorization on manifold. Pattern Recogn. Lett. 98, 68\u201374 (2017)","DOI":"10.1016\/j.patrec.2017.08.027"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Shang, R., Wang, W., Stolkin, R., Jiao, L.: Non-negative spectral learning and sparse regression-based dual-graph regularized feature selection. IEEE Trans. Cybern. 48(2), 793\u2013806 (2018)","DOI":"10.1109\/TCYB.2017.2657007"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"He, L., Ray, N., Guan, Y., Zhang, H.: Fast large-scale spectral clustering via explicit feature mapping. IEEE Trans. Cybern. 49(3), 1058\u20131071 (2019)","DOI":"10.1109\/TCYB.2018.2794998"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Jia, Y., Kwong, S., Hou, J.: Semi-supervised spectral clustering with structured sparsity regularization. IEEE Signal Process. Lett. 25(3), 403\u2013407 (2018)","DOI":"10.1109\/LSP.2018.2791606"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Fan, F., Ma, Y., Li, C., Mei, X., Huang, J., Ma, J.: Hyperspectral image denoising with superpixel segmentation and low-rank representation. Inf. Sci. 397, 48\u201368 (2017)","DOI":"10.1016\/j.ins.2017.02.044"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Lin, B., Tao, X., Lu, J.: Hyperspectral image denoising via matrix factorization and deep prior regularization. IEEE Trans. Image Process. 29, 565\u2013578 (2020)","DOI":"10.1109\/TIP.2019.2928627"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Li, Z., Tang, J., He, X.: Robust structured nonnegative matrix factorization for image representation. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1947\u20131960 (2018)","DOI":"10.1109\/TNNLS.2017.2691725"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548\u20131560 (2011)","DOI":"10.1109\/TPAMI.2010.231"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Shang, F., Jiao, L.C., Wang, F.: Graph dual regularization non-negative matrix factorization for co- clustering. Pattern Recognit. 45(6), 2237\u20132250 (2012)","DOI":"10.1016\/j.patcog.2011.12.015"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Shu, Z., Zhou, J., Huang, P., Yu, X., Yang, Z., Zhao, C.: Local and global regularized sparse coding for data representation. Neurocomputing 175, 188\u2013197 (2016)","DOI":"10.1016\/j.neucom.2015.10.048"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Han, J.: Locally consistent concept factorization for document clustering. IEEE Trans. Knowl. Data Eng. 23(6), 902\u2013913 (2011)","DOI":"10.1109\/TKDE.2010.165"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Ye, J., Jin, Z.: Dual-graph regularized concept factorization for clustering. Neurocomputing 138(11), 120\u2013130 (2014)","DOI":"10.1016\/j.neucom.2014.02.029"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Ma, J., Wu, J., Zhao, J., Jiang, J., Zhou, H., Sheng, Q.Z.: Nonrigid point set registration with robust transformation learning under manifold regularization. IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3584\u20133597 (2019)","DOI":"10.1109\/TNNLS.2018.2872528"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Liu, H., Yang, Z., Yang, J., Wu, Z., Li, X.: Local coordinate concept factorization for image representation. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1071\u20131082 (2014)","DOI":"10.1109\/TNNLS.2013.2286093"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Lu, M., Zhao, X. J., Zhang, L., Li, F. Z.: Semi-supervised concept factorization for document clustering. Inf. Sci. 331, 86\u201398 (2016)","DOI":"10.1016\/j.ins.2015.10.038"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Lu, M., Zhang, L., Zhao, X.J., Li, F.Z.: Constrained neighborhood preserving concept factorization for data representation. Knowl.-Based Syst. 102, 127\u2013139 (2016)","DOI":"10.1016\/j.knosys.2016.04.003"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Zhan, K., Shi, J., Wang, J., Tian, F.: Graph-regularized concept factorization for multi-view document clustering. Commun. Image Represent. 48, 411\u2013418 (2017)","DOI":"10.1016\/j.jvcir.2017.02.019"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Yan, W., Zhang, B., Ma, S., Yang, Z.: A novel regularized concept factorization for document clustering. Knowl.-Based Syst. 135, 147\u2013158 (2017)","DOI":"10.1016\/j.knosys.2017.08.010"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, J., Hu, J., Zhang, C., Liu, J.: Graph-based discriminative concept factorization for data representation. Knowl.-Based Syst. 118, 70\u201379 (2017)","DOI":"10.1016\/j.knosys.2016.11.012"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Li, X., Shen, X., Shu, Z., Ye, Q., Zhao, C.: Graph regularize multilayer concept factorization for data representation. Neurocomuting 238, 139\u2013151 (2017)","DOI":"10.1016\/j.neucom.2017.01.045"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Zhan, K., Shi, J., Wang, J., Wang, H., Xie, Y.: Adaptive structure concept factorization for multiview clustering. Neural Comput. 30(4), 1080\u20131103 (2018)","DOI":"10.1162\/neco_a_01055"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Pei, X., Chen, C., Gong, W.: Concept factorization with adaptive neighbors for document clustering. IEEE Trans. Neural Netw. Learn. Syst. 29(2), 343\u2013352 (2018)","DOI":"10.1109\/TNNLS.2016.2626311"},{"key":"13_CR40","unstructured":"Fan, R., Chung, K.: Spectral Graph Theory. Providence RI: American Mathematical Society (1997)"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Leng, C., Zhang, H., Cai, G., Cheng, I., Basu, A.: Graph regularized Lp smooth non-negative matrix factorization for data representation. IEEE\/CAA J. Autom. Sin. 16(2), 584\u2013595 (2019)","DOI":"10.1109\/JAS.2019.1911417"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624\u20131637 (2005)","DOI":"10.1109\/TKDE.2005.198"}],"container-title":["Lecture Notes in Computer Science","Smart Multimedia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82475-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T14:34:49Z","timestamp":1741012489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82475-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031824746","9783031824753"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82475-3_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Multimedia","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Los Angeles, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/smartmultimedia.org\/website\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}