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DYNAMIC COMPETITIVE PROBABILISTIC PRINCIPAL COMPONENTS ANALYSIS
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DOI：10.1142/S0129065709001860.png)
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Abstract
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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.
Keywords:
Unsupervised learning
local PCA
probabilistic PCA
competitive learning
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