Multi-label feature selection with constraint regression and adaptive spectral graph.

Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.