Abstract To solve the problem of multi-label data classification, a multi-label learning algorithm based on sparse representation is proposed. The testing samples are treated as a sparse linear combination of training samples, and the sparsest coefficients are obtained by using l1-minimization. Then, the discriminating information of sparse coefficients is utilized to calculate membership function of the testing sample. Finally, the labels are ranked according to the membership function and the classification is completed. Extensive experiments are conducted on gene functional analysis, natural scene classification and web page categorization, and experimental results demonstrate the effectiveness of the proposed method. The results also show that the proposed method based on sparse representation achieves better results than other algorithms.
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2012, Vol. 25 
