N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

10 Nov 2014 Makoto Yamada Avishek Saha Hua Ouyang Dawei Yin Yi Chang

We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of the non-negative least-angle regression (LARS) called N${}^3$LARS, where the similarity between input and output is measured through the normalized version of the Hilbert-Schmidt Independence Criterion (HSIC)... (read more)

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