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Feature selection has been proven a powerful preprocessing step forhigh-dimensional data analysis. However, most state-of-the-art methods tend tooverlook the structural correlation information between pairwise samples, whichmay encapsulate useful information for refining the performance of featureselection. Moreover, they usually consider candidate feature relevancyequivalent to selected feature relevancy, and some less relevant features maybe misinterpreted as salient features. To overcome these issues, we propose anew feature selection method using structural correlation between pairwisesamples. Our idea is based on converting the original vectorial features intostructure-based feature graph representations to incorporate structuralrelationship between samples, and defining a new evaluation measure to computethe joint significance of pairwise feature combinations in relation to thetarget feature graph. Furthermore, we formulate the corresponding featuresubset selection problem into a least square regression model associated with afused lasso regularizer to simultaneously maximize the joint relevancy andminimize the redundancy of the selected features. To effectively solve theoptimization problem, an iterative algorithm is developed to identify the mostdiscriminative features. Experiments demonstrate the effectiveness of theproposed approach.

Fused Lasso for Feature Selection using Structural Information