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Abstract:
This paper proposes an approach to classification of adjacent
segments of a time series as being either of
K
classes. We use a hierarchical model that consists of a feature
extraction stage and a generative classifier which is built on
top of these features. Such two stage approaches are often used
in signal and image processing. The novel part of our work is
that we link these stages probabilistically by using a
latent feature space
. To use
one
joint model is a Bayesian requirement, which has the advantage
to fuse information according to its certainty.
The classifier is implemented as hidden Markov model with
Gaussian and Multinomial observation distributions defined on a
suitably chosen representation of autoregressive models. The
Markov dependency is motivated by the assumption that successive
classifications will be correlated. Inference is done with Markov
chain Monte Carlo (MCMC) techniques. We apply the proposed
approach to synthetic data and to classification of EEG that was
recorded while the subjects performed different cognitive tasks.
All experiments show that using a latent feature space results in
a significant improvement in generalization accuracy. Hence we
expect that this idea generalizes well to other hierarchical
models.
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