Finding Alternate Features in Lasso

By Satoshi Hara, Takanori Maehara. 2016

5 citations recorded by the source corpus.

We propose a method for finding alternate features missing in the Lasso optimal solution. In ordinary Lasso problem, one global optimum is obtained and the resulting features are interpreted as task-relevant features. However, this can overlook possibly relevant features not selected by the Lasso. With the proposed method, we can provide not only the Lasso optimal solution but also possible alternate features to the Lasso solution. We show that such alternate features can be computed efficiently by avoiding redundant computations. We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features.

Subfields: Economics and Econometrics, Sociology and Political Science

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In Economics and Econometrics

1. Jiaxuan Wang
2. Jeeheh Oh
3. Jenna Wiens

Citations in the corpus

• Learning Credible Models (2017, background): “The most common forms of regularization, l 1 (LASSO) and l 2 θ represents the model parameters; β ∈ [ 0 , 1 ] is a hyperparameter that controls the tradeoff between the l 1 and l 2 norms; w is a set of non-negative weights for each feature; w ∗ | θ | [ i ] is the i th largest parameter sorted by…”