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Gradient-based kernel method for feature extraction and variable selection
Kenji Fukumizu · Chenlei Leng

Thu Dec 02:00 PM -- 12:00 AM PST @ Harrah’s Special Events Center 2nd Floor #None

We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a subset of predictors. First, a method of linear feature extraction is proposed using the gradient of regression function, based on the recent development of the kernel method. In comparison with other existing methods, the proposed one has wide applicability without strong assumptions on the regressor or type of variables, and uses computationally simple eigendecomposition, thus applicable to large data sets. Second, in combination of a sparse penalty, the method is extended to variable selection, following the approach by Chen et al. (2010). Experimental results show that the proposed methods successfully find effective features and variables without parametric models.

Author Information

Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP)
Chenlei Leng (University of Warwick)

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