Abstract When the specific features of labels are extracted by most of the existing label-specific features learning methods, only the correlations among labels are taken into account and the correlations among instances and among features are neglected. Therefore, the classification accuracy are reduced. To solve this problem, an algorithm for multi-label label-specific features learning combined with multi-category correlation information is designed in this paper. Label correlation, feature correlation and instance correlation are considered. The label correlation between labels are calculated by cosine similarity. The similarity graph matrix is constructed to learn feature correlation and instance correlation. The specific features of labels are selected by the proposed algorithm compactly, the classification accuracy is improved and the problem of excessive dimensionality in multi-label classification is effectively solved.
Fund:Supported by National Natural Science Foundation of China(No.61402004, 61370110), Key Project of Education Department of Anhui Province(No.KJ2018A0012)
Corresponding Authors:
GAO Qingwei, Ph.D., professor. His research interests include digital signal processing and digital image processing.
About author:: WU Anqi, master student. His research interests include machine learning. SUN Dong, Ph.D., associate professor. His research interests include digital image processing
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2020, Vol. 33 
