Multilabel Feature Selection Based on Fuzzy Mutual Information and Orthogonal Regression

With the increase of high-dimensional multilabel data, multilabel feature selection (MFS) has received more and more widespread attention. Embedded feature selection methods have been widely studied due to their high efficiency and low computational cost. Fuzzy mutual information, as an effective tool for processing continuous features, is widely used in filter feature selection, which results in many repeated entropy calculations. Most of the existing multilabel embedded feature selection methods are based on least squares regression, which loses a lot of statistical and structural information. To solve the above-mentioned problems, we established an optimization framework based on fuzzy mutual information that considers global correlation to obtain the weight of each feature. Under this framework, many repeated entropy operations are avoided. Then, the weight of each feature is introduced into the orthogonal regression optimization framework as prior knowledge. Finally, two optimization frameworks are comprehensively considered for MFS. Furthermore, considering the characteristics of multilabel data, we extend the proposed method to feature-specific MFS. We conducted sufficient experiments to demonstrate the efficiency of our proposed method.