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Dirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional Data

Dirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional Data

This video was recorded at 24th Annual International Conference on Machine Learning (ICML), Corvallis 2007. Proportional data (normalized histograms) have been frequently occurring in various areas, and they could be mathematically abstracted as points residing in a geometric simplex. A proper distance metric on this simplex is of importance in many applications including classification and information retrieval. In this paper, we develop a novel framework to learn an optimal metric on the simplex. Ma jor features of our approach include: 1) its flexibility to handle correlations among bins/dimensions; 2) widespread applicability without being limited to ad hoc backgrounds; and 3) a "real" global solution in contrast to existing traditional local approaches. The technical essence of our... Show More

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