310 citations · 607 across the 39 of their papers we have counts for
Showing 2012 · stat.MLShow all
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stat.ML2012★ 16 cited
Feature Selection via L1-Penalized Squared-Loss Mutual Information
Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to s…
stat.ML2012★ 310 cited
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal +2
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operato…