Finding Alternate Features in Lasso
This addresses feature selection limitations in Lasso for researchers and practitioners, but it is incremental as it builds on existing Lasso methods.
The paper tackles the problem that Lasso's single optimal solution may overlook relevant features, proposing a method to efficiently compute alternate features, demonstrated on the 20 newsgroup data to find reasonable alternatives.
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.