期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 30, 期 6, 页码 1081-1094出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2785795
关键词
Global and local label correlation; label manifold; missing labels; multi-label learning
类别
资金
- NSFC [61333014]
- Collaborative Innovation Center of Novel Software Technology and Industrialization
- program B for Outstanding PhD candidate of Nanjing University
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
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