Robust classification of functional and quantitative image data using functional mixed models.

ABSTRACT: This article introduces new methods for performing classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between-function correlations. The methods include training and prediction steps. In the training steps we train the FMM model by treating class designation as one of the fixed effects, and in the prediction steps we classify the new objects using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the methods can be used in any FMM framework, we provide details

SUBMITTER: Zhu H 

PROVIDER: S-EPMC3443537 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature