{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:47:42Z","timestamp":1742914062492,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030368012"},{"type":"electronic","value":"9783030368029"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36802-9_19","type":"book-chapter","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T18:03:03Z","timestamp":1575568983000},"page":"168-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Label Embedding Method for Multi-label Classification via Exploiting Local Label Correlations"],"prefix":"10.1007","author":[{"given":"Xidong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"19_CR1","unstructured":"Balasubramanian, K., Lebanon, G.: The landmark selection method for multiple output prediction. In: 29th International Conference on Machine Learning, pp. 983\u2013990. OmniPress, Madison (2012)"},{"key":"19_CR2","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Singapore (2006)"},{"key":"19_CR3","unstructured":"Cao, L., Xu, J.: A label compression coding approach through maximizing dependence between features and labels for multi-label classification. In: 27th International Joint Conference on Neural Networks, pp. 1\u20138. IEEE Press, New York (2015)"},{"issue":"10","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1109\/TNNLS.2013.2296501","volume":"25","author":"F Charte","year":"2014","unstructured":"Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: LI-MLC: a label inference methodology for addressing high dimensionality in the label space for multilabel classification. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1842\u20131854 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR5","unstructured":"Chen, Y., Lin, H.: Feature-aware label space dimension reduction for multi-label classification. In: 26th Annual Conference on Neural Information Processing Systems, pp. 1538\u20131546. MIT Press, Cambridge (2012)"},{"key":"19_CR6","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/11564089_7","volume-title":"Algorithmic Learning Theory","author":"A Gretton","year":"2005","unstructured":"Gretton, A., Bousquet, O., Smola, A., Sch\u00f6lkopf, B.: Measuring statistical dependence with Hilbert-Schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63\u201377. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564089_7"},{"key":"19_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41111-8","volume-title":"Multilabel Classification: Problem Analysis, Metrics and Techniques","author":"F Herrera","year":"2016","unstructured":"Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J.: Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer, Switzerland (2016). https:\/\/doi.org\/10.1007\/978-3-319-41111-8"},{"key":"19_CR9","unstructured":"Hsu, D., Kakade, S., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. In: 23rd Annual Conference on Neural Information Processing Systems, pp. 772\u2013780. MIT Press, Cambridge (2009)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Huang, S.J., Zhou, Z.H.: Multi-label learning by exploting label correlations locally. In: 26th AAAI Conference on Artificial Intelligence, pp. 949\u2013955. AAAI Press, New York (2012)","DOI":"10.1609\/aaai.v26i1.8287"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Wicker, J., Pfahringer, B., Kramer, S.: Multi-label classification using Boolean matrix decomposition. In: 27th Annual ACM Symposium on Applied Computing, pp. 179\u2013186. ACM Press, New York (2012)","DOI":"10.1145\/2245276.2245311"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Jain, H., Prabhu, Y., Varma, M.: Extreme multi-label loss functions for recommendation, tagging, ranking and other missing label applications. In: 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 435\u2013944. ACM Press, New York (2016)","DOI":"10.1145\/2939672.2939756"},{"issue":"4","key":"19_CR13","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1109\/TNN.2009.2015760","volume":"20","author":"Y Jia","year":"2009","unstructured":"Jia, Y., Nie, F., Zhang, C.: Trace ratio problem revisited. IEEE Trans. Neural Netw. 20(4), 729\u2013735 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"6","key":"19_CR14","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.1109\/TNNLS.2017.2691545","volume":"29","author":"Z Lin","year":"2018","unstructured":"Lin, Z., Ding, G., Han, J., Shao, L.: End-to-end feature-aware label space encoding for multilabel classification with many classes. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2472\u20132487 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR15","unstructured":"Lin, Z., Ding, G., Hu, M., Wang, J.: Multi-label classification via feature-aware implicit label space encoding. In: 31st International Conference on Machine Learning, pp. 325\u2013333. JMLR Website (2014)"},{"key":"19_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-319-46675-0_45","volume-title":"Neural Information Processing","author":"J Luo","year":"2016","unstructured":"Luo, J., Cao, L., Xu, J.: A non-linear label compression coding method based on five-layer auto-encoder for multi-label classification. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 415\u2013424. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46675-0_45"},{"issue":"9","key":"19_CR17","doi-asserted-by":"publisher","first-page":"2508","DOI":"10.1162\/NECO_a_00320","volume":"24","author":"F Tai","year":"2012","unstructured":"Tai, F., Lin, H.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508\u20132542 (2012)","journal-title":"Neural Comput."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Wang, H., Nie, F., Huang, H.: Globally and locally consistent unsupervised projection. In: 28th AAAI Conference on Artificial Intelligence, pp. 1328\u20131333. AAAI Press, New York (2014)","DOI":"10.1609\/aaai.v28i1.8915"},{"key":"19_CR19","unstructured":"Xu, J., Mao, Z.H.: Multilabel feature extraction algorithm via maximizing approximated and symmetrized normalized cross-covariance operator. IEEE Trans. Cybern. 1\u201314 (2019, in press)"},{"key":"19_CR20","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.engappai.2015.07.023","volume":"45","author":"JJ Zhang","year":"2015","unstructured":"Zhang, J.J., Feng, M., Wang, H., Li, X.: Depedence maximization based label space dimension reduction for multi-label classification. Eng. Appl. Artif. Intell. 45, 453\u2013463 (2015)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, M.L., Zhang, K.: Multi-label learning by exploiting label dependency. In: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 999\u20131007. ACM Press, New York (2010)","DOI":"10.1145\/1835804.1835930"},{"issue":"1\u20132","key":"19_CR22","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10994-011-5276-1","volume":"88","author":"T Zhou","year":"2012","unstructured":"Zhou, T., Tao, D., Wu, X.: Compressed labeling on distilled labelsets for multi-label learning. Mach. Learn. 88(1\u20132), 69\u2013126 (2012)","journal-title":"Mach. Learn."},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, W.J., Yu, Y., Zhang, M.L.: Binary linear compression for multi-label classification. In: 26th International Joint Conference on Artificial Intelligence, pp. 3546\u20133552. IJCAI Website (2017)","DOI":"10.24963\/ijcai.2017\/496"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36802-9_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:51:17Z","timestamp":1710262277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-36802-9_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030368012","9783030368029"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36802-9_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"5 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ajiips.com.au\/iconip2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}