Feature Selection for Latent Factor Models
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909567934267392 |
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| author | Kansabanik, Rittwika Barbu, Adrian |
| author_facet | Kansabanik, Rittwika Barbu, Adrian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Feature Selection for Latent Factor Models Kansabanik, Rittwika Barbu, Adrian Machine Learning Applications 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. |
| title | Feature Selection for Latent Factor Models |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2412.10128 |