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Yamada et al., 2014 - Google Patents

High-dimensional feature selection by feature-wise kernelized lasso

Yamada et al., 2014

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Document ID
14440307900279341174
Author
Yamada M
Jitkrittum W
Sigal L
Xing E
Sugiyama M
Publication year
Publication venue
Neural computation

External Links

Snippet

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 operator (Lasso) allows computationally efficient feature selection based on linear …
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