Graph-based normalization and whitening for non-linear data analysis

In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for “global normalization”; we then adapt these meas...

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Bibliographic Details
Published in:Neural networks Vol. 19; no. 6; pp. 864 - 876
Main Author: Aaron, Catherine
Format: Journal Article Conference Proceeding
Language:English
Published: Oxford Elsevier Ltd 01.07.2006
Elsevier Science
Elsevier
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Cluster Analysis
Computer Graphics
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Geodesic distance
Graph
Humans
Mathematics
Models, Statistical
Nonlinear Dynamics
Normalization
Other Statistics
SOM
Statistics
SOM
Normalization
Graph
Geodesic distance
Data analysis
Procedure call
Multigraph
Geodesic structure
Neural network
Algorithm
Weighted graph
Self-organising feature maps
Geodesic
Analogy
Dispersion measure
normalization
graph
geodesic distance
ISSN:0893-6080, 1879-2782
Online Access:Get full text
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Summary:In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for “global normalization”; we then adapt these measures using a weighted graph to build a local normalization called “graph-based” normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the “global” and the “local” problem.
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ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2006.05.022