Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning

摘要

In this paper we show that manifold learning could effectively find the essential dimension of nonlinear high-dimensional data, but it could not use class label information of the data because it is an unsupervised learning method. This paper explores a novel supervised feature selection algorithm based on low-rank and manifold learning. Specifically, we obtain the coefficient matrix according to the relationship between data and class label. Then we combine sparse learning and manifold learning to conduct feature selection. Finally, we use the low-rank representation to further adjust the result of feature selection. Experimental results show that our new method obtains the best results on the four public datasets when compared with six existing methods.