Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 20, Issue 9, Pages 1474-1489Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2009.2025888
Keywords
Competitive learning; dimensionality reduction; handwritten digit recognition; probabilistic principal component analysis (PPCA); self-organizing maps (SOMs); unsupervised learning
Categories
Funding
- Ministry of Education and Science of Spain [TIC2003-03067, TIN2006-07362]
- Autonomous Government of Andalusia (Spain) [P06-TIC-01615, P07-TIC02800]
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In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low complexity on the dimensionality of the input space. This allows to process very high-dimensional data to obtain reliable estimations of the probability densities which are based on the PPCA framework. Experimental results are presented, which show the map formation capabilities of the proposal with high-dimensional data, and its potential in image and video compression applications.
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