PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm R. Jenssen, T. Eltoft, M. Girolami, D. Erdogmus:
Jenssen Robert, Eltoft T, Girolami Mark and Erdogmus D
In: NIPS 2006, 07 Dec 2006, Vancouver.

Abstract

We propose a new kernel-based data transformation technique. It is founded on the principle of maximum entropy (MaxEnt) preservation, hence named kernel MaxEnt. The key measure is Renyi's entropy estimated via Parzen windowing. We show that kernel MaxEnt is based on eigenvectors, and is in that sense similar to kernel PCA, but may produce strikingly different transformed data sets. An enhanced spectral clustering algorithm is proposed, by replacing kernel PCA by kernel MaxEnt as an intermediate step. This has a major impact on performance.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2963
Deposited By:Mark Girolami
Deposited On:08 March 2007