Fused lasso for feature selection using structural information

Most state-of-the-art feature selection methods tend to overlook the structural relationship between a pair of samples associated with each feature dimension, 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 therefore, some less relevant features may be misinterpreted as salient features. To overcome these issues, we propose a new feature selection method based on graph-based feature representations and the Fused Lasso framework in this paper. Unlike stateof-the-art feature selection approaches, our method has two main advantages. First, it can accommodate structural relationship between a pair of samples through a graph-based feature representation. Second, our method can enhance the trade-off between the relevancy of each individual feature on the one hand and its redundancy between pairwise features on the other. This is achieved through the use of a Fused Lasso framework applied to features reordered on the basis of their relevance with respect to the target feature. To effectively solve the optimization problem, an iterative algorithm is developed to identify the most discriminative features. Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A novel feature selection method is proposed in this paper based on graph-based feature representations and the Fused Lasso framework, aiming to address issues such as overlooking structural relationships between samples, equating candidate feature relevancy with selected feature relevancy. The method uses an iterative algorithm to identify the most discriminative features and outperforms competitors on benchmark datasets.