Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA

1 code implementation • 15 Dec 2015 • Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan

In this paper, we clarify the behavior of VB learning in probabilistic PCA (or fully-observed matrix factorization).

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering

no code implementations • NeurIPS 2013 • Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

However, Bayesian learning is often obstructed by computational difficulty: the rigorous Bayesian learning is intractable in many models, and its variational Bayesian (VB) approximation is prone to suffer from local minima.

Clustering Computational Efficiency

Sparse Bayesian Methods for Low-Rank Matrix Estimation

no code implementations • 25 Feb 2011 • S. Derin Babacan, Martin Luessi, Rafael Molina, Aggelos K. Katsaggelos

Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications.

Matrix Completion