{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:00:00Z","timestamp":1781215200091,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>In real world applications, data are often with multiple modalities. Researchers proposed the multi-modal learning approaches for integrating the information from different modalities. Most of the previous multi-modal methods assume that training examples are with complete modalities. However, due to the failures of data collection, self-deficiencies and other various reasons, multi-modal examples are usually with incomplete feature representation in real applications. In this paper, the incomplete feature representation issues in multi-modal learning are named as incomplete modalities, and we propose a semi-supervised multi-modal learning method aimed at this incomplete modal issue (SLIM). SLIM can utilize the extrinsic information from unlabeled data against the insufficiencies brought by the incomplete modal issues in a semi-supervised scenario. Besides, the proposed SLIM forms the problem into a unified framework which can be treated as a classifier or clustering learner, and integrate the intrinsic consistencies and extrinsic unlabeled information. As SLIM can extract the most discriminative predictors for each modality, experiments on 15 real world multi-modal datasets validate the effectiveness of our method.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/416","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2998-3004","source":"Crossref","is-referenced-by-count":24,"title":["Semi-Supervised Multi-Modal Learning with Incomplete Modalities"],"prefix":"10.24963","author":[{"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De-Chuan","family":"Zhan","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang-Rong","family":"Sheng","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:52:34Z","timestamp":1530769954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/416"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/416","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}