35 citations · 113 across the 20 of their papers we have counts for
8 papers · 1 filter
Multilinear Common Component Analysis via Kronecker Product Representation
Kohei Yoshikawa, Shuichi Kawano
We consider the problem of extracting a common structure from multiple tensor datasets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker…
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
Kazuaki. Murayama, Shuichi. Kawano
In the variational relevance vector machine, the gamma distribution is representative as a hyperprior over the noise precision of automatic relevance determination prior. Instead o…
Sparse principal component regression via singular value decomposition approach
Shuichi Kawano
Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage constructs a regression model whose…
Bayesian sparse convex clustering via global-local shrinkage priors
Kaito Shimamura, Shuichi Kawano
Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted norm is usually e…
Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization
Kohei Yoshikawa, Shuichi Kawano
We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. T…
Sparse principal component regression for generalized linear models
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are…
Sparse principal component regression with adaptive loading
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variab…
Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions
Shuichi Kawano
This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from tha…