A local information-based feature-selection algorithm for data regression

Authors:
Xinjun Peng;Dong Xu
Affiliations:
Department of Mathematics, Shanghai Normal University, Shanghai 200234, PR China and Scientific Computing Key Laboratory of Shanghai Universities, Shanghai 200234, PR China;Department of Mathematics, Shanghai Normal University, Shanghai 200234, PR China
Venue:
Pattern Recognition
Year:
2013

Citing 22
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Abstract

This paper presents a novel feature-selection algorithm for data regression with a lot of irrelevant features. The proposed method is based on well-established machine-learning technique without any assumption about the underlying data distribution. The key idea in this method is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local information, and to learn globally feature relevance within the least squares loss framework. In contrast to other feature-selection algorithms for data regression, the learning of this method is efficient since the solution can be readily found through gradient descent with a simple update rule. Experiments on some synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem and the effectiveness of our algorithm.