35 citations · 113 across the 20 of their papers we have counts for
20 papers
Multi-Task Learning Regression via Convex Clustering
Akira Okazaki, Shuichi Kawano
Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL,…
Multivariate regression modeling in integrative analysis via sparse regularization
Shuichi Kawano, Toshikazu Fukushima, Junichi Nakagawa +1
The multivariate regression model basically offers the analysis of a single dataset with multiple responses. However, such a single-dataset analysis often leads to unsatisfactory r…
Bayesian Fused Lasso Modeling via Horseshoe Prior
Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano
Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a…
Multi-task Learning for Compositional Data via Sparse Network Lasso
Akira Okazaki, Shuichi Kawano
A network lasso enables us to construct a model for each sample, which is known as multi-task learning. Existing methods for multi-task learning cannot be applied to compositional…
A Bayesian approach to multi-task learning with network lasso
Kaito Shimamura, Shuichi Kawano
Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. A characteristic of network lasso is setting a different mode…
Smoothly varying ridge regularization
Daeju Kim, Shuichi Kawano, Yoshiyuki Ninomiya
A basis expansion with regularization methods is much appealing to the flexible or robust nonlinear regression models for data with complex structures. When the underlying function…
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…
Variable fusion for Bayesian linear regression via spike-and-slab priors
Shengyi Wu, Kaito Shimamura, Kohei Yoshikawa +2
In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents…
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…
Bayesian generalized fused lasso modeling via NEG distribution
Kaito Shimamura, Masao Ueki, Shuichi Kawano +1
The fused lasso penalizes a loss function by the norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we p…
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…
Selection of tuning parameters in bridge regression models via Bayesian information criterion
Shuichi Kawano
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parame…
Adaptive bridge regression modeling with model selection criteria
Shuichi Kawano
We consider the problem of constructing an adaptive bridge regression modeling, which is a penalized procedure by imposing different weights to different coefficients in the bridge…
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…
Varying-coefficient modeling via regularized basis functions
Hidetoshi Matsui, Toshihiro Misumi, Shuichi Kawano
We address the problem of constructing varying-coefficient models based on basis expansions along with the technique of regularization. A crucial point in our modeling procedure is…
Semi-supervised logistic discrimination for functional data
Shuichi Kawano, Sadanori Konishi
Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the con…