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Heuristic-Algorithms-for-Feature-Selection-under-Bayesian-Models-with-Block-diagonal-Covariance-Structure

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.gitattributes		.gitattributes
APP_TMNC_sub.m		APP_TMNC_sub.m
APP_TMNC_sub2.m		APP_TMNC_sub2.m
GSGjp.m		GSGjp.m
GSGpp.m		GSGpp.m
POFAC_gaussian.m		POFAC_gaussian.m
POFAC_sub.m		POFAC_sub.m
REMAIN_gaussian.m		REMAIN_gaussian.m
REMAIN_sub.m		REMAIN_sub.m
SPM_gaussian.m		SPM_gaussian.m
SPM_sub.m		SPM_sub.m
TwoMNC_Robust_gaussian.m		TwoMNC_Robust_gaussian.m
igib_sub.m		igib_sub.m
logGammaMD.m		logGammaMD.m
obf_gaussian.m		obf_gaussian.m
readme.txt		readme.txt

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obf_gaussian is optimal Bayesian filter assuming Gaussian features.
TwoMNC_Robust is 2MNC_Robust assuming Gaussian features.
REMAIN_gaussian is REMAIN for Gaussian features.
POFAC_gaussian is POFAC assuming Gaussian features.
SPM_gaussian is SPM assuming Gaussian features.

Refer to each code for details how to run them, and refer to the following paper
for details about each algorithm:

Heuristic Algorithms for Feature Selection under Bayesian Models with Block-diagonal Covariance Structure
by A Foroughipour and L A Dalton