Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction

Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analy...

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Bibliographic Details
Published in:Data mining and knowledge discovery 2002-04, Vol.6 (2), p.173
Main Authors: Madigan, David, Raghavan, Nandini, Dumouchel, William, Nason, Martha, Posse, Christian, Ridgeway, Greg
Format: Article
Language:English
Subjects:
Algorithms
Data compression
Datasets
Random variables
Regression analysis
Statistical analysis
Online Access:Get full text
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Summary:Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analyses carried out on the original dataset. Likelihood-based data squashing (LDS) differs from a previously published squashing algorithm insofar as it uses a statistical model to squash the data. The results show that LDS provides excellent squashing performance even when the target statistical analysis departs from the model used to squash the data.
ISSN:1384-5810
1573-756X
DOI:10.1023/A:1014095614948