一种新的多标记特征提取方法

doi:10.3969/j.issn.1001-2400.2016.06.011

Abstract

Abstract:

Existing multi-label feature extraction methods are limited by not fully exploiting feature information. To tackle this problem， this paper proposes a new method for multi-label feature extraction. First, it maximizes the Hilbert-Schmidt independence criterion (HSIC) between labels and the features after reducing dimensionality to exploit label information, while it minimizes the information loss using principal component analysis (PCA). Experiments across Yahoo demonstrate the effectiveness and superiority of the proposed method to PCA and 3 state-of-art multi-label feature extraction methods.

Key words: multi-label classification, feature extraction, Hilbert-Schmidt independence criterion, principal component analysis

ZHANG Jujie;FANG Min;GUO Jin. New method for multi-label feature extraction[J].Journal of Xidian University, 2016, 43(6): 62-67.

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New method for multi-label feature extraction

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