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 offers a theoretically sound guarantee of feature recovery under specific assumptions and has been demonstrated to outperform some existing feature selection methods on standard classification datasets. Additionally, it can be easily applied in a class-incremental learning setup, yielding performance comparable to that of some current methods.

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