Reduced Rank Multivariate Generalized Linear Models for Feature Extraction

Details Reduced Rank Multivariate Generalized Linear Models for Feature Extraction Reduced Rank Multivariate Generalized Linear Models for Feature Extraction

2010-07-19

arxiv.org/abs/1007.3098v2

Statistics

Machine Learning

Scientific paper

Supervised linear feature extraction corresponds to fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained multivariate generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature extraction. Through solving the rank constraint form of the problem, we propose progressive feature space reduction for fast computation in high dimensions with little performance loss. A novel projective cross-validation is proposed for parameter tuning in such nonconvex setups. Real data applications are given to show the power of the methodology in supervised dimension reduction and feature extraction.

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