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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2013-11-12
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Global Solvers for Variational Bayesian Low-rank Subspace Clustering
Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-37
Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning,
is generally per... [more]
IBISML 2012-11-08
Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. On Dimensionality Recovery Guarantee of Variational Bayesian PCA
Shinichi Nakajima (Nikon), Ryota Tomioka (Univ. of Tokyo), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) IBISML2012-66
The variational Bayesian (VB) approach is
one of the best tractable approximations to the Bayesian estimation,
and it ... [more]
IBISML 2011-11-09
Nara Nara Womens Univ. Generalization of Matrix Factorization for Robust PCA
Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) IBISML2011-61
Principal component analysis (PCA) can be regarded as approximating a
data matrix with
a low-rank one by imposing spar... [more]
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
Hokkaido   Global Solution of Variational Bayesian Matrix Factorization Under Matrix-wise Independence
Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) PRMU2011-58 IBISML2011-17
Variational Bayesian matrix factorization (VBMF) efficiently
approximates the posterior distribution of factorized mat... [more]
PRMU2011-58 IBISML2011-17
IBISML 2011-03-29
Osaka Nakanoshima Center, Osaka Univ. On Automatic Dimensionality Selection in Probabilistic PCA
Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech./JST), Derin Babacan (Illinois of.Univ.) IBISML2010-123
In probabilistic PCA,
the fully Bayesian estimation is computationally intractable.
To cope with this problem,
two ty... [more]
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