Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis

Donald C. Wunsch; David J. Morris; Rui Xu

Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis

Donald C. Wunsch
, David J. Morris
, Rui Xu

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.

Original language	American English
Journal	Default journal
State	Published - Jan 1 2014

Disciplines

Electrical and Computer Engineering
Numerical Analysis and Scientific Computing

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Adaptive Resonance Theory and Diffusion Maps for Clustering AppliFinal published version, 4.38 MBLicense: Unspecified

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title = "Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis",

abstract = " Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.",

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T1 - Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis

AU - Wunsch, Donald C.

AU - Morris, David J.

AU - Xu, Rui

PY - 2014/1/1

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N2 - Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.

AB - Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.

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M3 - Article

JO - Default journal

JF - Default journal

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Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis

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