Kernel Entropy Component Analysis

We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projecti...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 32; no. 5; pp. 847 - 860
Main Author: Jenssen, R.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2010
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
clustering
Clustering algorithms
Computer science; control theory; systems
Computer Simulation
Data processing. List processing. Character string processing
Database Management Systems
Databases, Factual
Eigenvalues
Eigenvalues and eigenfunctions
Entropy
Exact sciences and technology
Information Storage and Retrieval - methods
Information theory
Information, signal and communications theory
Intelligence
Kernel
kernel PCA
Kernels
Laplace equations
Machine intelligence
Memory organisation. Data processing
Models, Theoretical
Multidimensional systems
Noise reduction
Parzen windowing
Pattern analysis
pattern denoising
Pattern Recognition, Automated - methods
Principal component analysis
Reduction
Renyi entropy
Software
Spectra
Spectral data transformation
Statistics
Telecommunications and information theory
Transformations
Cluster analysis
Spectral method
Noise reduction
Spectral data transformation
Data transformation
Entropy
kernel PCA
Modeling
Data reduction
pattern denoising
Dimension reduction
Parzen windowing
Eigenvalue problem
clustering
Pattern analysis
Artificial intelligence
Principal component analysis
Signal to noise ratio
Information theory
Renyi entropy
ISSN:0162-8828, 1939-3539, 1939-3539
Online Access:Get full text
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Summary:We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.
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ISSN:0162-8828
1939-3539
1939-3539
DOI:10.1109/TPAMI.2009.100