Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression | IEEE Journals & Magazine | IEEE Xplore

Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression


Abstract:

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 fr...Show More

Abstract:

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 that minimizes the overall coding length of the segmented data, subject to a given distortion. By analyzing the coding length/rate of mixed data, we formally establish some strong connections of data segmentation to many fundamental concepts in lossy data compression and rate-distortion theory. We show that a deterministic segmentation is approximately the (asymptotically) optimal solution for compressing mixed data. We propose a very simple and effective algorithm that depends on a single parameter, the allowable distortion. At any given distortion, the algorithm automatically determines the corresponding number and dimension of the groups and does not involve any parameter estimation. Simulation results reveal intriguing phase-transition-like behaviors of the number of segments when changing the level of distortion or the amount of outliers. Finally, we demonstrate how this technique can be readily applied to segment real imagery and bioinformatic data.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 29, Issue: 9, September 2007)
Page(s): 1546 - 1562
Date of Publication: 13 August 2007

ISSN Information:

PubMed ID: 17627043

References

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