Supervised Feature Selection via Dependence Estimation

by Le Song, Alex Smola, Arthur Gretton, Karsten M. Borgwardt and Justin Bedo

Feature selection has been mentioned in a KDR paper. This paper uses HSIC and backward greedy algorithm. That's each time they pick up a dimension to throw away while the rest has the highest HSIC. I thought they might throw away the one with lowest HSIC but they enjoyed using HSIC as an lower bound of dependence.

The paper also mentions the connection between HSIC and other criterion such as MMD in a prevously scanned paper and KTA (it looks like an uncentered version of HSIC).