A new, cellular automaton-based, nearest neighbor pattern classifier and its VLSI implementation

  • Authors:

  • Panagiotis G. Tzionas;Philippos G. Tsalides;Adonios Thanailakis

  • Affiliations:

  • Department of Electrical Engineering, Democritus University of Thrace, Greece;Department of Electrical Engineering, Democritus University of Thrace, Greece;Department of Electrical Engineering, Democritus University of Thrace, Greece

  • Venue:
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Year:
  • 1994

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Abstract

A new, parallel, nearest-neighbor (NN) pattern classifier, based on a 2-D Cellular Automaton (CA) architecture, is presented in this paper. The proposed classifier is both time and space efficient, when compared with already existing NN classifiers, since it does not require complex distance calculations and ordering of distances, and storage requirements are kept minimal since each cell stores information only about its nearest neighborhood. The proposed classifier produces piece-wise linear discriminant curves between clusters of points of complex shape (nonlinearly separable) using the computational geometry concept known as the Voronoi diagram, which is established through CA evolution. These curves are established during an "off-line" operation and, thus, the subsequent classification of unknown patterns is achieved very fast. The VLSI design and implementation of a nearest neighborhood processor of the proposed 2-D CA architecture is also presented in this paper.