Bayesian Classification of Multiclass Functional Data

TL;DR

This paper introduces a Bayesian framework for classifying multiclass functional data using various multinomial models with basis expansions, and compares these methods through simulations and a phoneme dataset.

Contribution

It develops a Bayesian approach with finite random series priors for multiclass functional data classification, including model averaging and posterior contraction analysis.

Findings

Bayesian methods outperform traditional classifiers in simulations

Model averaging improves classification accuracy

Effective classification on phoneme dataset achieved

Abstract

We propose a Bayesian approach to estimating parameters in multiclass functional models. Unordered multinomial probit, ordered multinomial probit and multinomial logistic models are considered. We use finite random series priors based on a suitable basis such as B-splines in these three multinomial models, and classify the functional data using the Bayes rule. We average over models based on the marginal likelihood estimated from Markov Chain Monte Carlo (MCMC) output. Posterior contraction rates for the three multinomial models are computed. We also consider Bayesian linear and quadratic discriminant analyses on the multivariate data obtained by applying a functional principal component technique on the original functional data. A simulation study is conducted to compare these methods on different types of data. We also apply these methods to a phoneme dataset.