Feature Selection for Latent Factor Models

• URL: http://arxiv.org/abs/2412.10128v1
• Date: Fri, 13 Dec 2024 13:20:10 GMT
• Title: Feature Selection for Latent Factor Models
• Authors: Rittwika Kansabanik, Adrian Barbu,
• Abstract summary: Feature selection is crucial for pinpointing relevant features in high-dimensional datasets. 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.
• Score: 2.07180164747172
• License:
• 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.

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