{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:47:01Z","timestamp":1761896821573},"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>Zero-shot learning (ZSL) has been widely researched and get successful in machine learning. Most existing ZSL methods aim to accurately recognize objects of unseen classes by learning a shared mapping from the feature space to a semantic space. However, such methods did not investigate in-depth whether the mapping can precisely reconstruct the original visual feature. Motivated by the fact that the data have low intrinsic dimensionality e.g. low-dimensional subspace. In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations. Taking the encoder-decoder paradigm, the encoder part aims to learn a low-rank mapping from the visual feature to the semantic space, while decoder part manages to reconstruct the original data with the learned mapping. In addition, a non-greedy iterative algorithm is adopted to solve our model. Extensive experiments on six benchmark datasets demonstrate its superiority over several state-of-the-art algorithms.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/345","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"2490-2496","source":"Crossref","is-referenced-by-count":42,"title":["Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder"],"prefix":"10.24963","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, China"}]},{"given":"Quanxue","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, China"}]},{"given":"Jin","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, China"}]},{"given":"Jungong","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}]},{"given":"Ling","family":"Shao","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","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":"2018-07-05T01:51:49Z","timestamp":1530755509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/345"}},"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\/345","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}