Feature Selection for Latent Factor Models

TL;DR

This paper introduces a novel class-specific feature selection method using low-rank models and SNR criteria, providing theoretical guarantees and improved performance on classification tasks.

Contribution

It proposes a new class-specific feature selection approach with theoretical guarantees, outperforming existing methods on standard datasets.

Findings

The method achieves better classification accuracy.

It provides true feature recovery guarantees.

Outperforms existing feature selection techniques.

Abstract

Feature selection is crucial for pinpointing relevant features in high-dimensional datasets, mitigating the 'curse of dimensionality,' and enhancing machine learning performance. Traditional feature selection methods for classification use data from all classes to select features for each class. This paper explores feature selection methods that select features for each class separately, using class models based on low-rank generative methods and introducing a signal-to-noise ratio (SNR) feature selection criterion. This novel approach has theoretical true feature recovery guarantees under certain assumptions and is shown to outperform some existing feature selection methods on standard classification datasets.