Online Group Feature Selection

TL;DR

This paper introduces a novel online group feature selection method that processes feature groups as they arrive, combining spectral analysis and Lasso to select discriminative and globally optimal features, outperforming existing methods.

Contribution

The paper formulates the online group feature selection problem and proposes a two-stage approach integrating spectral analysis and Lasso for effective feature selection.

Findings

Outperforms state-of-the-art online feature selection methods

Effective in real-world applications like image analysis and spam filtering

Demonstrates robustness on benchmark datasets

Abstract

Online feature selection with dynamic features has become an active research area in recent years. However, in some real-world applications such as image analysis and email spam filtering, features may arrive by groups. Existing online feature selection methods evaluate features individually, while existing group feature selection methods cannot handle online processing. Motivated by this, we formulate the online group feature selection problem, and propose a novel selection approach for this problem. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we use spectral analysis to select discriminative features in each group when it arrives. In the inter-group selection, we use Lasso to select a globally optimal subset of features. This 2-stage procedure continues until there are no more features to come or some predefined stopping conditions are met. Extensive experiments conducted on benchmark and real-world data sets demonstrate that our proposed approach outperforms other state-of-the-art online feature selection methods.