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ICML
2007
IEEE

Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data

10 years 4 days ago
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data
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. Major 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 approach is to fit a parametric distribution to the observed empirical data in the simplex. The distribution is parameterized by affinities between simplex vertices, which is learned via maximizing likelihood of observed data. Then, these affinities induce a metric on the simplex, defined as the ear...
Hua-Yan Wang, Hongbin Zha, Hong Qin
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Hua-Yan Wang, Hongbin Zha, Hong Qin
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