Paper Title:
Geometry Covering Theory in Biomimetic Pattern Cognition
  Abstract

We describe a new pattern recognition method which is based on the concept of geometry theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite a kind of mapping, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
73-78
DOI
10.4028/www.scientific.net/AMR.204-210.73
Citation
W. M. Cao, X. F. Li, H. Feng, "Geometry Covering Theory in Biomimetic Pattern Cognition", Advanced Materials Research, Vols. 204-210, pp. 73-78, 2011
Online since
February 2011
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Price
$32.00
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