Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

Details Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

2009-10-31

arxiv.org/abs/0911.0054v1

Computer Science

Learning

Scientific paper

The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain strong convexity property of general exponential families, which allow their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L_1 regularization.

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