Local energy-based framework for feature ranking

Abstract

Feature selection has been a research topic with practical significance in pattern recognition, machine learning and data mining. In this paper, a local energy-based framework is proposed to estimate the features' relevance for ranking them. The key idea behind this framework is to transform a complex nonlinear problem into a set of locally linear ones through local energy-based learning. Moreover, the convergence of this framework is analyzed. Some experiments are conducted on benchmark data sets including high dimension small sample size data, such as gene data. The experimental results have shown the correctness of our algorithm derived from this framework and its performance is higher or similar to other classical feature ranking algorithms in most cases.