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

Authors:
Catherine Aaron
Affiliations:
SAMOS-MATISSE, Paris, France
Venue:
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Year:
2006

Citing 1
Cited 1

Self-organizing maps

Self-organizing maps

2012 Special Issue: Autonomous Growing Neural Gas for applications with time constraint: Optimal parameter estimation

Neural Networks

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Abstract

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.