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Latent Topic-aware Multi-Label Classification

In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels. In our study, we assume that there exists a common feature representation for labels in each topic. Then, feature-label correlation can be exploited in the latent topic space. This paper shows that the sample and feature exaction, which are two important procedures for removing noisy and redundant information encoded in training samples in both sample and feature perspectives, can be effectively and efficiently performed in the latent topic space by considering topic-based feature-label correlation. Empirical studies on several benchmarks demonstrate the effectiveness and efficiency of the proposed topic-aware framework.

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