Learning correlation information for multi-label feature selection

In many real-world multi-label applications, the content of multi-label data is usually characterized by high dimensional features, which contains complex correlation information, i.e., label correlations and redundant features. To alleviate the problem, we present a novel scheme, called learning correlation information for multi-label feature selection (LCIFS) method, by jointly digging up label correlations and controlling feature redundancy. To be specific, the regression model via manifold framework is presented to fit the relationship between feature space and label distribution, during which adaptive spectral graph is leveraged to learn more precise structural correlations of labels simultaneously. Besides, we utilize the relevance of features to constrain the redundancy of the generated feature subset, and a general ℓ2,p-norm regularized model is employed to fulfill more robust feature selection. The proposed method is transformed into an explicit optimization function, which is conquered by an efficient iterative optimization algorithm. Finally, we conduct comprehensive experiments on twelve realistic multi-label datasets, including text domain, image domain, and audio domain. The statistic results demonstrate the effectiveness and superiority of the proposed method among nine competition methods.