1,187 research outputs found
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
The probabilistic principal component analysis (PPCA) is built upon a global
linear mapping, with which it is insufficient to model complex data variation.
This paper proposes a mixture of bilateral-projection probabilistic principal
component analysis model (mixB2DPPCA) on 2D data. With multi-components in the
mixture, this model can be seen as a soft cluster algorithm and has capability
of modeling data with complex structures. A Bayesian inference scheme has been
proposed based on the variational EM (Expectation-Maximization) approach for
learning model parameters. Experiments on some publicly available databases
show that the performance of mixB2DPPCA has been largely improved, resulting in
more accurate reconstruction errors and recognition rates than the existing
PCA-based algorithms
Parsimonious Shifted Asymmetric Laplace Mixtures
A family of parsimonious shifted asymmetric Laplace mixture models is
introduced. We extend the mixture of factor analyzers model to the shifted
asymmetric Laplace distribution. Imposing constraints on the constitute parts
of the resulting decomposed component scale matrices leads to a family of
parsimonious models. An explicit two-stage parameter estimation procedure is
described, and the Bayesian information criterion and the integrated completed
likelihood are compared for model selection. This novel family of models is
applied to real data, where it is compared to its Gaussian analogue within
clustering and classification paradigms
Mixtures of Skew-t Factor Analyzers
In this paper, we introduce a mixture of skew-t factor analyzers as well as a
family of mixture models based thereon. The mixture of skew-t distributions
model that we use arises as a limiting case of the mixture of generalized
hyperbolic distributions. Like their Gaussian and t-distribution analogues, our
mixture of skew-t factor analyzers are very well-suited to the model-based
clustering of high-dimensional data. Imposing constraints on components of the
decomposed covariance parameter results in the development of eight flexible
models. The alternating expectation-conditional maximization algorithm is used
for model parameter estimation and the Bayesian information criterion is used
for model selection. The models are applied to both real and simulated data,
giving superior clustering results compared to a well-established family of
Gaussian mixture models
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