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Selecting Diverse Features via Spectral Regularization

Selecting Diverse Features via Spectral Regularization

This video was recorded at Video Journal of Machine Learning Abstracts - Volume 3. We study the problem of diverse feature selection in linear regression: selecting a small subset of diverse features that can predict a given objective. Diversity is useful for several reasons such as interpretability, robustness to noise, etc. We propose several spectral regularizers that capture a notion of diversity of features and show that these are all submodular set functions. These regularizers, when added to the objective function for linear regression, result in approximately submodular functions, which can then be maximized approximately by efficient greedy and local search algorithms, with provable guarantees. We compare our algorithms to traditional greedy and ℓ1-regularization schemes and show that we obtain a more diverse set of features that result in the regression problem being stable under perturbations.


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