Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization

Details Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization

2010-01-18

arxiv.org/abs/1001.3090v2

Computer Science

Information Theory

5 pages, 4 figures, submitted to ISIT 2010

Scientific paper

This paper concerns the construction of tests for universal hypothesis testing problems, in which the alternate hypothesis is poorly modeled and the observation space is large. The mismatched universal test is a feature-based technique for this purpose. In prior work it is shown that its finite-observation performance can be much better than the (optimal) Hoeffding test, and good performance depends crucially on the choice of features. The contributions of this paper include: 1) We obtain bounds on the number of \epsilon distinguishable distributions in an exponential family. 2) This motivates a new framework for feature extraction, cast as a rank-constrained optimization problem. 3) We obtain a gradient-based algorithm to solve the rank-constrained optimization problem and prove its local convergence.

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