@inproceedings{chartier_febam_2007,

title = {FEBAM: A feature-extracting bidirectional associative memory},

author = {S. Chartier and G. Giguère and P. Renaud and J. -M. Lina and R. Proulx},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-51749114880&doi=10.1109%2fIJCNN.2007.4371210&partnerID=40&md5=d929ff11969da516dec3fb7a11e742a1},

doi = {10.1109/IJCNN.2007.4371210},

isbn = {1-4244-1380-X 978-1-4244-1380-5},

year  = {2007},

date = {2007-01-01},

booktitle = {IEEE International Conference on Neural Networks - Conference Proceedings},

pages = {1679–1684},

address = {Orlando, FL},

abstract = {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. ©2007 IEEE.},

note = {ISSN: 10987576},

keywords = {Artificial intelligence, Associative processing, Bi-directional associative memory, Blind source separation, Computer networks, Data storage equipment, Feature extraction, Financial data processing, Hemodynamics, Image processing, Image reconstruction, Independent component analysis, Joint conference, Neural networks, Separation},

pubstate = {published},

tppubtype = {inproceedings}

}