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Closed-form Supervised Dimensionality Reduction with Generalized Linear Models
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. We propose a family of supervised dimensionality reduction (SDR) algorithms that combine feature extraction (dimensionality reduction) with learning a predictive model in a unified optimization framework, using data- and class-appropriate generalized linear models (GLMs), and handling both classification and regression problems. Our approach uses simple closed-form update rules and is provably convergent. Promising empirical results are demonstrated on a variety of high-dimensional datasets.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
