A robust graph based multi-label feature selection considering feature-label dependency.

Feature selection for multilabel data is a challenging and meaningful work. The information contained in multilabel data is more abundant, which may help further mine knowledge and aid decision-making in various real-life applications. However, the difficulty also increases in dealing with multilabel data because the relations between labels and features need to be considered simultaneously. Missing labels and noises may exist in multilabel data, which may affect the feature selection process. Aiming at solving these problems, a robust feature selection approach is constructed under the sparse learning framework based on the least squares regression model in this study. First, a novel objective function is built by considering the robustness of the method and the manifold information. Nonnegative matrix factorization (NMF) is used to compress the label matrix to reduce false label information, which may mislead the feature selection process. The l_2,1-norm is adopted to constrain the least squares regression term. Manifold regularizers are used to construct low-dimensional manifold embeddings of the original feature and label space, retaining the local manifold structure of the data. Furthermore, the correlations between features and labels are explored, and an improved weight matrix is designed. Then, an iteration algorithm is proposed to solve the objective function. Extensive experiments are performed to analyze the proposed approach, which is compared with state-of-the-art algorithms on public multilabel datasets. The experimental results verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]