Result: Optimizing the cauchy-schwarz PDF distance for information theoretic, non-parametric clustering

Title:
Optimizing the cauchy-schwarz PDF distance for information theoretic, non-parametric clustering
Authors:
JENSSEN, Robert, ERDOGMUS, Deniz, HILD, Kenneth E, PRINCIPE, Jose C, ELTOFT, Torbjørn
Source:
Energy minimization methods in computer vision and pattern recognition (5th international workshop, EMMCVPR 2005, St. Augustine FL, USA, November 9-11, 2005)Lecture notes in computer science. :34-45
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 16 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse amas, Cluster analysis, Analisis cluster, Approximation stochastique, Stochastic approximation, Aproximación estocástica, Classification, Clasificación, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Complexité calcul, Computational complexity, Complejidad computación, Descente gradient, Gradient descent, Gradient bajada, Estimation densité, Density estimation, Estimación densidad, Fonction densité probabilité, Probability density function, Función densidad probabilidad, Gradient, Gradiente, Mesure densité, Density measurement, Medición densidad, Optimisation, Optimization, Optimización, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Théorie information, Information theory, Teoría información, Vision ordinateur, Computer vision, Visión ordenador
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Physics, University of Tromsø, 9037 Tromsø, Norway
Department of Computer Science and Engineering, Oregon Graduate Institute, OHSU, Portland, OR. 97006, United States
Department of Radiology, University of California, San Francisco, CA. 94143, United States
Department of Electrical and Computer Engineering, University of Florida, Gainesville FL. 32611, United States
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17412963
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17412963
Database:
PASCAL Archive

Further Information

This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data patterns, in order to maximize the recent Cauchy-Schwarz (CS) probability density function (pdf) distance measure. Each pdf corresponds to a cluster. The CS distance is estimated analytically and non-parametrically by means of the Parzen window technique for density estimation. The resulting form of the cost function makes it possible to develop an efficient adaption procedure based on constrained gradient descent, using stochastic approximation of the gradients. The computational complexity of the algorithm is O(MN), M << N, where N is the total number of data patterns and M is the number of data patterns used in the stochastic approximation. We show that the new algorithm is capable of performing well on several odd-shaped and irregular data sets.