Learning the Information Divergence

no code implementations • 5 Jun 2014 • Onur Dikmen, Zhirong Yang, Erkki Oja

Here we present a framework that facilitates automatic selection of the best divergence among a given family, based on standard maximum likelihood estimation.

BIG-bench Machine Learning Topic Models

Clustering by Nonnegative Matrix Factorization Using Graph Random Walk

no code implementations • NeurIPS 2012 • Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja

Nonnegative Matrix Factorization (NMF) is a promising relaxation technique for clustering analysis.

Clustering

Heavy-Tailed Symmetric Stochastic Neighbor Embedding

no code implementations • NeurIPS 2009 • Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja

Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.

Data Visualization