Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation

Abstract

SUMMARY We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the pro-hibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time. key words: multi-label feature selection, multivariate feature selection, feature dependency, Nystr¨om method