• Corpus ID: 4857366

# Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables

@inproceedings{Takada2018IndependentlyIL,
title={Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables},
author={Masaaki Takada and Taiji Suzuki and Hironori Fujisawa},
booktitle={AISTATS},
year={2018}
}
• Published in AISTATS 6 November 2017
• Computer Science
Sparse regularization such as $\ell_1$ regularization is a quite powerful and widely used strategy for high dimensional learning problems. The effectiveness of sparse regularization has been supported practically and theoretically by several studies. However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features. Ordinary $\ell_1$ regularization can select variables correlated with each other, which results in…

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