  Feature selection has been proven a powerful preprocessing step for
high-dimensional data analysis. However, most state-of-the-art methods tend to
overlook the structural correlation information between pairwise samples, which
may encapsulate useful information for refining the performance of feature
selection. Moreover, they usually consider candidate feature relevancy
equivalent to selected feature relevancy, and some less relevant features may
be misinterpreted as salient features. To overcome these issues, we propose a
new feature selection method using structural correlation between pairwise
samples. Our idea is based on converting the original vectorial features into
structure-based feature graph representations to incorporate structural
relationship between samples, and defining a new evaluation measure to compute
the joint significance of pairwise feature combinations in relation to the
target feature graph. Furthermore, we formulate the corresponding feature
subset selection problem into a least square regression model associated with a
fused lasso regularizer to simultaneously maximize the joint relevancy and
minimize the redundancy of the selected features. To effectively solve the
optimization problem, an iterative algorithm is developed to identify the most
discriminative features. Experiments demonstrate the effectiveness of the
proposed approach.
