Robust Unsupervised Feature Selection
Abstract
A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. Unlike traditional unsupervised feature selection methods, pseudo cluster labels are learned via local learning regularized robust nonnegative matrix factorization. During the label learning process, feature selection is performed simultaneously by robust joint l<sub>2, 1</sub> norms minimization. Since RUFS utilizes l<sub>2, 1</sub> norm minimization on processes of both label learning and feature learning, outliers and noise could be effectively handled and redundant or noisy features could be effectively reduced. Our method adopts the advantages of robust nonnegative matrix factorization, local learning, and robust feature learning. In order to make RUFS be scalable, we design a (projected) limited-memory BFGS based iterative algorithm to efficiently solve the optimization problem of RUFS in terms of both memory consumption and computation complexity. Experimental results on different benchmark real world datasets show the promising performance of RUFS over the state-of-the-arts.