{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:51:48Z","timestamp":1781877108882,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538263","type":"print"},{"value":"9789819538270","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3827-0_3","type":"book-chapter","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:02:54Z","timestamp":1781874174000},"page":"35-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise-Free Laplacian Learning for Multiple Kernel Spectral Clustering"],"prefix":"10.1007","author":[{"given":"Feng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfei","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiu","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"issue":"2","key":"3_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TAI.2021.3065894","volume":"2","author":"G Chao","year":"2021","unstructured":"Chao, G., Sun, S., Bi, J.: A survey on multiview clustering. IEEE Trans. Artif. Intell. 2(2), 146\u2013168 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"113070","DOI":"10.1016\/j.cma.2020.113070","volume":"367","author":"Q Cheng","year":"2020","unstructured":"Cheng, Q., Liu, C., Shen, J.: A new Lagrange multiplier approach for gradient flows. Comput. Methods Appl. Mech. Eng. 367, 113070 (2020)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"12","key":"3_CR3","doi-asserted-by":"publisher","first-page":"12350","DOI":"10.1109\/TKDE.2023.3270311","volume":"35","author":"U Fang","year":"2023","unstructured":"Fang, U., Li, M., Li, J., Gao, L., Jia, T., Zhang, Y.: A comprehensive survey on multi-view clustering. IEEE Trans. Knowl. Data Eng. 35(12), 12350\u201312368 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Gao, H., Nie, F., Li, X., Huang, H.: Multi-view subspace clustering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4238\u20134246 (2015)","DOI":"10.1109\/ICCV.2015.482"},{"issue":"3","key":"3_CR5","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.patcog.2012.09.002","volume":"46","author":"M G\u00f6nen","year":"2013","unstructured":"G\u00f6nen, M., Alpayd\u0131n, E.: Localized algorithms for multiple kernel learning. Pattern Recogn. 46(3), 795\u2013807 (2013)","journal-title":"Pattern Recogn."},{"issue":"3","key":"3_CR6","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1109\/TPAMI.2019.2945574","volume":"43","author":"A Khan","year":"2021","unstructured":"Khan, A., Maji, P.: Approximate graph Laplacians for multimodal data clustering. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 798\u2013813 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"3895","DOI":"10.1109\/TNNLS.2021.3054789","volume":"33","author":"A Khan","year":"2022","unstructured":"Khan, A., Maji, P.: Multi-manifold optimization for multi-view subspace clustering. IEEE Trans. Neural Netw. Learn. Syst. 33, 3895\u20133907 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"3","key":"3_CR8","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1007\/s11042-022-13428-4","volume":"82","author":"D Khurana","year":"2023","unstructured":"Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: state of the art, current trends and challenges. Multimedia tools Appl. 82(3), 3713\u20133744 (2023)","journal-title":"Multimedia tools Appl."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/TPAMI.2020.3011148","volume":"44","author":"X Li","year":"2022","unstructured":"Li, X., Zhang, H., Wang, R., Nie, F.: Multiview clustering: a scalable and parameter-free bipartite graph fusion method. IEEE Trans. Pattern Anal. Mach. Intell. 44, 330\u2013344 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"3_CR10","first-page":"3418","volume":"34","author":"W Liang","year":"2022","unstructured":"Liang, W., et al.: Multi-view spectral clustering with high-order optimal neighborhood Laplacian matrix. IEEE Trans. Knowl. Data Eng. 34(7), 3418\u20133430 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"3_CR11","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1007\/s11280-022-01135-x","volume":"26","author":"R Lin","year":"2023","unstructured":"Lin, R., Tang, F., He, C., Wu, Z., Yuan, C., Tang, Y.: DIRS-KG: a kg-enhanced interactive recommender system based on deep reinforcement learning. World Wide Web (WWW) 26(5), 2471\u20132493 (2023)","journal-title":"World Wide Web (WWW)"},{"issue":"6","key":"3_CR12","first-page":"2872","volume":"34","author":"J Liu","year":"2022","unstructured":"Liu, J., et al.: Optimal neighborhood multiple kernel clustering with adaptive local kernels. IEEE Trans. Knowl. Data Eng. 34(6), 2872\u20132885 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"05","key":"3_CR13","first-page":"1191","volume":"42","author":"X Liu","year":"2020","unstructured":"Liu, X., et al.: Multiple kernel $$ k $$ k-means with incomplete kernels. IEEE Trans. Pattern Anal. Mach. Intell. 42(05), 1191\u20131204 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TIP.2023.3340609","volume":"33","author":"H Lu","year":"2024","unstructured":"Lu, H., Xu, H., Wang, Q., Gao, Q., Yang, M., Gao, X.: Efficient multi-view k-means for image clustering. IEEE Trans. Image Process. 33, 273\u2013284 (2024)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.patrec.2018.12.004","volume":"150","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Robust multi-view continuous subspace clustering. Pattern Recogn. Lett. 150, 306\u2013312 (2021)","journal-title":"Pattern Recogn. Lett."},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Mao, S., Zhan, J., Li, J., Jiang, Y.: Knowledge structure-aware graph-attention networks for knowledge tracing. In: International Conference on Knowledge Science, Engineering and Management, pp. 309\u2013321. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-10983-6_24","DOI":"10.1007\/978-3-031-10983-6_24"},{"issue":"3","key":"3_CR17","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/TLT.2023.3259013","volume":"16","author":"S Mao","year":"2023","unstructured":"Mao, S., Zhan, J., Wang, Y., Jiang, Y.: Improving knowledge tracing via considering two types of actual differences from exercises and prior knowledge. IEEE Trans. Learn. Technol. 16(3), 324\u2013338 (2023)","journal-title":"IEEE Trans. Learn. Technol."},{"issue":"1","key":"3_CR18","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TPAMI.2017.2780166","volume":"41","author":"D Marin","year":"2019","unstructured":"Marin, D., Tang, M., Ayed, I.B., Boykov, Y.: Kernel clustering: density biases and solutions. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 136\u2013147 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.patrec.2016.11.008","volume":"85","author":"V Mijangos","year":"2017","unstructured":"Mijangos, V., Sierra, G., Montes, A.: Sentence level matrix representation for document spectral clustering. Pattern Recogn. Lett. 85, 29\u201334 (2017)","journal-title":"Pattern Recogn. Lett."},{"key":"3_CR20","unstructured":"Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14 (2001)"},{"issue":"12","key":"3_CR21","doi-asserted-by":"publisher","first-page":"5509","DOI":"10.1109\/TNNLS.2020.2968848","volume":"31","author":"X Peng","year":"2020","unstructured":"Peng, X., Feng, J., Zhou, J.T., Lei, Y., Yan, S.: Deep subspace clustering. IEEE Trans. Neural Networks Learn. Syst. 31(12), 5509\u20135521 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Sun, M., et al.: Scalable multi-view subspace clustering with unified anchors. In: Proceedings of the ACM International Conference on Multimedia, pp. 3528\u20133536 (2021)","DOI":"10.1145\/3474085.3475516"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Wang, L., Xing, J., Yin, M., Huang, X.: Multi-view spectral clustering with adaptive local neighbors. In: Proceedings of the International Symposium on Parallel Architectures, Algorithms and Programming, pp. 157\u2013161 (2021)","DOI":"10.1109\/PAAP54281.2021.9720444"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Wang, P., Wu, D., Wang, R., Nie, F.: Multi-view graph clustering via efficient global-local spectral embedding fusion. In: Proceedings of the ACM International Conference on Multimedia, pp. 3268\u20133276 (2023)","DOI":"10.1145\/3581783.3612190"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Wang, R., Lu, J., Lu, Y., Nie, F., Li, X.: Discrete multiple kernel k-means. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3111\u20133117 (2021)","DOI":"10.24963\/ijcai.2021\/428"},{"issue":"12","key":"3_CR26","doi-asserted-by":"publisher","first-page":"5910","DOI":"10.1109\/TIP.2019.2916740","volume":"28","author":"J Wu","year":"2019","unstructured":"Wu, J., Lin, Z., Zha, H.: Essential tensor learning for multi-view spectral clustering. IEEE Trans. Image Process. 28(12), 5910\u20135922 (2019)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"3_CR27","doi-asserted-by":"publisher","first-page":"8402","DOI":"10.1007\/s10489-022-03898-2","volume":"53","author":"H Yin","year":"2023","unstructured":"Yin, H., Wang, G., Hu, W., Zhang, Z.: Fine-grained multi-view clustering with robust multi-prototypes representation. Appl. Intell. 53(7), 8402\u20138420 (2023)","journal-title":"Appl. Intell."},{"issue":"10","key":"3_CR28","doi-asserted-by":"publisher","first-page":"4676","DOI":"10.1109\/TKDE.2020.3045770","volume":"34","author":"P Zhang","year":"2022","unstructured":"Zhang, P., et al.: Consensus one-step multi-view subspace clustering. IEEE Trans. Knowl. Data Eng. 34(10), 4676\u20134689 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR29","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.patrec.2023.10.027","volume":"176","author":"B Zhou","year":"2023","unstructured":"Zhou, B., Liu, W., Shen, M., Lu, Z., Zhang, W., Zhang, L.: Adaptive graph fusion learning for multi-view spectral clustering. Pattern Recogn. Lett. 176, 102\u2013108 (2023)","journal-title":"Pattern Recogn. Lett."},{"issue":"4","key":"3_CR30","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1109\/TNNLS.2019.2919900","volume":"31","author":"S Zhou","year":"2020","unstructured":"Zhou, S., et al.: Multiple kernel clustering with neighbor-kernel subspace segmentation. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1351\u20131362 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, S., et al.: Multi-view spectral clustering with optimal neighborhood Laplacian matrix. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 6965\u20136972. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i04.6180"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3827-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:02:58Z","timestamp":1781874178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3827-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819538263","9789819538270"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3827-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"20 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}