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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2010-05, Vol.32 (5), p.847-860
1. Verfasser:	Jenssen, R.
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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