{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T02:08:41Z","timestamp":1770689321945,"version":"3.49.0"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["grant No. 61402311"],"award-info":[{"award-number":["grant No. 61402311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangsu Province Colleges and Universities Natural Science Research Project","award":["grant No. 13KJB520021"],"award-info":[{"award-number":["grant No. 13KJB520021"]}]},{"DOI":"10.13039\/100000001","name":"the U.S. National Science Foundation","doi-asserted-by":"crossref","award":["IIS-1115417"],"award-info":[{"award-number":["IIS-1115417"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:p> Multi-label active learning for image classification has been a popular research topic. It faces several challenges, even though related work has made great progress. Existing studies on multi-label active learning do not pay attention to the cleanness of sample data. In reality, data are easily polluted by external influences that are likely to disturb the exploration of data space and have a negative effect on model training. Previous methods of label correlation mining, which are purely based on observed label distribution, are defective. Apart from neglecting noise influence, they also cannot acquire sufficient relevant information. In fact, they neglect inner relation mapping from example space to label space, which is an implicit way of modeling label relationships. To solve these issues, we develop a novel multi-label active learning with low-rank application (ENMAL) algorithm in this paper. A low-rank model is constructed to quantize noise level, and the example-label pairs that contain less noise are emphasized when sampling. A low-rank mapping matrix is learned to signify the mapping relation of a multi-label domain to capture a more comprehensive and reasonable label correlation. Integrating label correlation with uncertainty and considering sample noise, an efficient sampling strategy is developed. We extend ENMAL with automatic labeling (denoted as AL-ENMAL) to further reduce the annotation workload of active learning. Empirical research demonstrates the efficacy of our approaches. <\/jats:p>","DOI":"10.1142\/s0218001418500052","type":"journal-article","created":{"date-parts":[[2017,6,14]],"date-time":"2017-06-14T23:51:22Z","timestamp":1497484282000},"page":"1850005","source":"Crossref","is-referenced-by-count":12,"title":["An Active Learning Approach for Multi-Label Image Classification with Sample Noise"],"prefix":"10.1142","volume":"32","author":[{"given":"Jian","family":"Wu","sequence":"first","affiliation":[{"name":"The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anqian","family":"Guo","sequence":"additional","affiliation":[{"name":"The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, P. R. China"},{"name":"Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science &amp; Technology, Nanjing, P. R. China"},{"name":"Department of Computer Science, University of Central Arkansas, Conway 72035, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[{"name":"The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, P. R. 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