Search Results for author:
Found 4 papers, 3 papers with code
Exponentially Convergent Algorithms for Supervised Matrix Factorization
Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives.
Supervised Dictionary Learning with Auxiliary Covariates
Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives.
Pursuing Sources of Heterogeneity in Modeling Clustered Population
We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously.
Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection
We have used simulation studies and real data analysis to demonstrate the superior performance of hyper-LASSO priors, and to investigate the issues of choosing heaviness and scale of hyper-LASSO priors.
