Dynamic competitive probabilistic principal components analysis

Dynamic competitive probabilistic principal components analysis

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, s...

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Bibliographic Details
Published in:International journal of neural systems Vol. 19; no. 2; p. 91
Main Authors: López-Rubio, Ezequiel, Ortiz-DE-Lazcano-Lobato, Juan Miguel
Format: Journal Article
Language:English
Published: Singapore 01-04-2009
Subjects:
Algorithms
Artificial Intelligence
Models, Neurological
Models, Statistical
Neurons - physiology
Nonlinear Dynamics
Principal Component Analysis
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Description
Summary:We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.
ISSN:0129-0657
DOI:10.1142/S0129065709001860