Joint Laplacian feature weights learning

Abstract

Some filter methods stemming from statistics or geometry theory select features individually. Hence they neglect the combination of features and lead to suboptimal subset of features. To address this problem, a joint feature weights learning framework, which automatically determines the optimal size of the feature subset and selects the best features corresponding to a given adjacency graph, is proposed in this paper. In particular, our framework imposes nonnegative and l22-norm constraints on feature weights and iteratively learns feature weights jointly and simultaneously. A new minimization algorithm with proved convergence is also developed to optimize the non-convex objective function. Utilizing this framework as a tool, we propose a new unsupervised feature selection algorithm called Joint Laplacian Feature Weights Learning. Experimental results on five real-world datasets demonstrate the effectiveness of our algorithm.