Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation
Hyunki Lim, Jaesung Lee, Dae‐Won Kim · IEICE Transactions on Information and Systems · 2016
We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive 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.