A New Unsupervised Learning for Clustering Using Geometric Associative Memories

Authors:
Benjamín Cruz;Ricardo Barrón;Humberto Sossa
Affiliations:
Center for Computing Research, National Polytechnic Institute, México City, México 07738;Center for Computing Research, National Polytechnic Institute, México City, México 07738;Center for Computing Research, National Polytechnic Institute, México City, México 07738
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

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Abstract

Associative memories (AMs) have been extensively used during the last 40 years for pattern classification and pattern restoration. A new type of AMs have been developed recently, the so-called Geometric Associative Memories (GAMs), these make use of Conformal Geometric Algebra (CGA) operators and operations for their working. GAM's, at the beginning, were developed for supervised classification, getting good results. In this work an algorithm for unsupervised learning with GAMs will be introduced. This new idea is a variation of the k-means algorithm that takes into account the patterns of the a specific cluster and the patterns of another clusters to generate a separation surface. Numerical examples are presented to show the functioning of the new algorithm.