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Joint multilabel classification and feature selection based on deep canonical correlation analysis
Author(s) -
Dai Liang,
Du Guodong,
Zhang Jia,
Li Candong,
Wei Rong,
Li Shaozi
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5864
Subject(s) - canonical correlation , artificial intelligence , discriminative model , computer science , feature selection , machine learning , correlation , feature (linguistics) , embedding , exploit , selection (genetic algorithm) , pattern recognition (psychology) , class (philosophy) , task (project management) , mathematics , engineering , linguistics , philosophy , geometry , computer security , systems engineering
Summary In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.