Kernels for longitudinal data with variable sequence length and sampling intervals.

Abstract:

:We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.

journal_name

Neural Comput

journal_title

Neural computation

authors

Lu Z,Leen TK,Kaye J

doi

10.1162/NECO_a_00164

subject

Has Abstract

pub_date

2011-09-01 00:00:00

pages

2390-420

issue

9

eissn

0899-7667

issn

1530-888X

journal_volume

23

pub_type

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