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Knowl. Discov. Data"],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>\n            As an important machine learning task, multi-label learning deals with the problem where each sample instance (feature vector) is associated with multiple labels simultaneously. Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification. One potential drawback of this traditional strategy is that each label might have its own specific characteristics and using identical features for all label cannot lead to optimized performance. In this article, we propose an effective algorithm named LSDM, i.e.,\n            <jats:italic>leveraging label-specific discriminant mapping features for multi-label learning<\/jats:italic>\n            , to overcome the drawback. LSDM sets diverse ratio parameter values to conduct cluster analysis on the positive and negative instances of identical label. It reconstructs label-specific feature space which includes distance information and spatial topology information. Our experimental results show that combining these two parts of information in the new feature representation can better exploit the clustering results in the learning process. Due to the problem of diverse combinations for identical label, we employ simplified linear discriminant analysis to efficiently excavate optimal one for each label and perform classification by querying the corresponding results. Comparison with the state-of-the-art algorithms on a total of 20 benchmark datasets clearly manifests the competitiveness of LSDM.\n          <\/jats:p>","DOI":"10.1145\/3319911","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T17:12:14Z","timestamp":1556557934000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning"],"prefix":"10.1145","volume":"13","author":[{"given":"Yumeng","family":"Guo","sequence":"first","affiliation":[{"name":"Tongji University and The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fulai","family":"Chung","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guozheng","family":"Li","sequence":"additional","affiliation":[{"name":"China Academy of Chinese Medical Sciences and Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiancong","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James C.","family":"Gee","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btk048"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_2_1_3_1","first-page":"2","article-title":"Matrix completion for multi-label image classification","volume":"201","author":"Cabral Ricardo Silveira","year":"2011","unstructured":"Ricardo Silveira Cabral , Fernando De la Torre , Jo\u00e3o Paulo Costeira , and Alexandre Bernardino . 2011 . Matrix completion for multi-label image classification . In Proceedings of the Neural Information Processing Systems , Vol. 201. 2 . Ricardo Silveira Cabral, Fernando De la Torre, Jo\u00e3o Paulo Costeira, and Alexandre Bernardino. 2011. Matrix completion for multi-label image classification. In Proceedings of the Neural Information Processing Systems, Vol. 201. 2.","journal-title":"Proceedings of the Neural Information Processing Systems"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2343234"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5271-6"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1099"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5127-5"},{"key":"e_1_2_1_9_1","volume-title":"King","author":"Clare Amanda","year":"2001","unstructured":"Amanda Clare and Ross D . 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