Perfect Dimensionality Recovery by Variational Bayesian PCA

no code implementations • NeurIPS 2012 • Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan

The variational Bayesian (VB) approach is one of the best tractable approximations to the Bayesian estimation, and it was demonstrated to perform well in many applications.

Probabilistic Low-Rank Subspace Clustering

no code implementations • NeurIPS 2012 • S. D. Babacan, Shinichi Nakajima, Minh Do

In this paper, we consider the problem of clustering data points into low-dimensional subspaces in the presence of outliers.

Clustering Density Estimation

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent

no code implementations • NeurIPS 2011 • Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan

A recent study on fully-observed VBMF showed that, under a stronger assumption that the two factorized matrices are column-wise independent, the global optimal solution can be analytically computed.