CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels
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by
Zhiwei Li, Lu Sun
2022
Abstract
A variety of modern applications exhibit multi-view multi-label learning,
where each sample has multi-view features, and multiple labels are correlated
via common views. In recent years, several methods have been proposed to cope
with it and achieved much success, but still suffer from two key problems: 1)
lack the ability to deal with the incomplete multi-view weak-label data, in
which only a subset of features and labels are provided for each sample; 2)
ignore the presence of noisy views and tail labels usually occurring in
real-world problems. In this paper, we propose a novel method, named CEMENT, to
overcome the limitations. For 1), CEMENT jointly embeds incomplete views and
weak labels into distinct low-dimensional subspaces, and then correlates them
via Hilbert-Schmidt Independence Criterion (HSIC). For 2), CEMEMT adaptively
learns the weights of embeddings to capture noisy views, and explores an
additional sparse component to model tail labels, making the low-rankness
available in the multi-label setting. We develop an alternating algorithm to
solve the proposed optimization problem. Experimental results on seven
real-world datasets demonstrate the effectiveness of the proposed method.
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