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IJCNN
2007
IEEE

FEBAM: A Feature-Extracting Bidirectional Associative Memory

10 years 3 months ago
FEBAM: A Feature-Extracting Bidirectional Associative Memory
—In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)-inspired architecture, the resulting model inherits properties such as attractor-like behavior and successful processing of noisy inputs, while being able to achieve principal component analysis (PCA) tasks such as feature extraction and dimensionality reduction. The model is tested by simulating image reconstruction and blind source separation tasks. Simulations show that the model fares particularly well compared to current neural PCA and independent component analysis (ICA) algorithms. It is argued the model possesses more cognitive explanative power than any other nonlinear/linear PCA and ICA algorithm.
Sylvain Chartier, Gyslain Giguère, Patrice
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors Sylvain Chartier, Gyslain Giguère, Patrice Renaud, Jean-Marc Lina, Robert Proulx
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