TY - GEN
T1 - A new unsupervised learning for clustering using geometric associative memories
AU - Cruz, Benjamín
AU - Barrón, Ricardo
AU - Sossa, Humberto
N1 - Funding Information:
This work has been supported by the National Polytechnic Institute of Mexico (SIP-IPN), under grants 20090620 and 20091421, by the Mexican Science and Technology National Council (CONACyT) under grants 46805 and 182938, and the European Comission and CONACyT under grant FONCICYT 93829.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78651236240&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10268-4_28
DO - 10.1007/978-3-642-10268-4_28
M3 - Contribución a la conferencia
SN - 3642102670
SN - 9783642102677
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 246
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
T2 - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
Y2 - 15 November 2009 through 18 November 2009
ER -