Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression

In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmentation th...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 9; pp. 1546 - 1562
Main Authors:	Yi Ma, Derksen, H., Wei Hong, Wright, J.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.09.2007 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Artificial Intelligence Asymptotic properties Bioinformatics Coding Compressing Computer science; control theory; systems Computer Simulation Data Clustering Data compression Data Compression - methods Data Interpretation, Statistical Data Segmentation Distortion Exact sciences and technology Gaussian distribution Image coding Image segmentation Lossy Coding Lossy Compression Maximum likelihood estimation Microarray Data Clustering Models, Statistical Multivariate Analysis Multivariate Mixed Data Normal Distribution Optimization Parameter estimation Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Phase distortion Rate Distortion Segmentation Segments Signal processing algorithms Studies Cluster analysis Parameter estimation Multivariate mixed data Image processing Data compression Degenerate system DNA chip data clustering Modeling Phase transitions Outlier lossy coding Classification System identification Pattern analysis Deterministic approach Bioinformatics microarray data clustering Mixed distribution lossy compression Rate distortion theory data segmentation Image segmentation rate distortion Gaussian process Optimal solution Asymptotic approximation Artificial intelligence
ISSN:	0162-8828, 1939-3539
Online Access:	Get full text
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