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T-Obuchi's Projects

acceleratedcvon2dtvlr icon acceleratedcvon2dtvlr

This MATLAB package enables to efficiently compute leave-one-out cross validation error for linear regression with two regularization terms: L_1 and total-variation. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time.

acceleratedcvonmlr_matlab icon acceleratedcvonmlr_matlab

This MATLAB package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. Python version: https://github.com/T-Obuchi/AcceleratedCVonMLR_python

acceleratedcvonmlr_python icon acceleratedcvonmlr_python

This Python package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. MATLAB version: https://github.com/T-Obuchi/AcceleratedCVonMLR_matlab

ampr_lasso_matlab icon ampr_lasso_matlab

Bootstrap resampling is used to estimate confidence interval of variables in Lasso (some famous methods are bolasso and stability selection). This MATLAB package performs this in an efficient manner by conducting the resampling in a semi-analytic manner, enabling to avoid numerical resampling. Python translation is available: https://github.com/T-Obuchi/AMPR_lasso_python

ampr_lasso_python icon ampr_lasso_python

Bootstrap resampling is used to estimate confidence interval of variables in Lasso (some famous methods are bolasso and stability selection). This MATLAB package performs this in an efficient manner by conducting the resampling in a semi-analytic manner, enabling to avoid numerical resampling. MATLAB version is available: https://github.com/T-Obuchi/AMPR_lasso_matlab

graphbix icon graphbix

Graph clustering by Bayesian inference with cross-validation model assessment

slrpackage_acceleratedcv_matlab icon slrpackage_acceleratedcv_matlab

Sparse linear regression package with accelerated cross-validation. L_1, SCAD, MCP penalties are covered. The algorithm for optimization is cyclic coordinate descent.

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