Variational Discriminant Analysis with Variable Selection

12/17/2018
by   Weichang Yu, et al.
0

A Bayesian method that seamlessly fuses classification via discriminant analysis and hypothesis testing is developed. Building upon the original discriminant analysis classifier, modelling components are added to identify discriminative variables. A combination of cake priors and a novel form of variational Bayes we call reverse collapsed variational Bayes gives rise to variable selection that can be directly posed as a multiple hypothesis testing approach using likelihood ratio statistics. Some theoretical arguments are presented showing that Chernoff-consistency (asymptotically zero type I and type II error) is maintained across all hypotheses. We apply our method on some publicly available genomic datasets and show that our method performs well in practice. An R package VaDA has also been made available on Github.

READ FULL TEXT
research
12/10/2018

Variational Nonparametric Discriminant Analysis

Variable selection and classification methods are common objectives in t...
research
07/04/2018

Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data

We introduce a new method of performing high dimensional discriminant an...
research
09/19/2021

Uncertainty quantification for robust variable selection and multiple testing

We study the problem of identifying the set of active variables, termed ...
research
09/19/2017

varbvs: Fast Variable Selection for Large-scale Regression

We introduce varbvs, a suite of functions written in R and MATLAB for re...
research
11/20/2015

Bayesian SPLDA

In this document we are going to derive the equations needed to implemen...
research
08/23/2022

Variable selection and basis learning for ordinal classification

We propose a method for variable selection and basis learning for high-d...
research
08/16/2022

E-Statistics, Group Invariance and Anytime Valid Testing

We study worst-case growth-rate optimal (GROW) E-variables for hypothesi...

Please sign up or login with your details

Forgot password? Click here to reset