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Found 9 papers, 3 papers with code
Multilinear Common Component Analysis via Kronecker Product Representation
We consider the problem of extracting a common structure from multiple tensor datasets.
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
It is confirmed that a multiple kernel relevance vector regression model with good predictive accuracy can be obtained by selecting the scale parameter minimizing extended prediction information criterion.
Variable fusion for Bayesian linear regression via spike-and-slab priors
This is called variable fusion.
Sparse principal component regression via singular value decomposition approach
The proposed method enables us to obtain principal component loadings that possess information about both explanatory variables and a response variable.
Bayesian sparse convex clustering via global-local shrinkage priors
Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering.
Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization
To overcome this issue, we develop an estimation algorithm with rank and variable selection via sparse regularization and manifold optimization, which enables us to obtain an accurate estimation of the coefficient parameter even if the true rank of the coefficient parameter is high.
Sparse principal component regression for generalized linear models
The basic loss function is based on a combination of the regression loss and PCA loss.
Bayesian generalized fused lasso modeling via NEG distribution
The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both.
Sparse principal component regression with adaptive loading
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 variables.
