Testing component independence using data compressors

Authors:
Daniil Ryabko
Affiliations:
IDSIA, Manno, Switzerland
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 6
Cited 0

An introduction to Kolmogorov complexity and its applications (2nd ed.)

An introduction to Kolmogorov complexity and its applications (2nd ed.)
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection

The Journal of Machine Learning Research
Algorithmic Clustering of Music Based on String Compression

Computer Music Journal
The similarity metric

IEEE Transactions on Information Theory
Clustering by compression

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a new nonparametric test for component independence which is based on application of data compressors to ranked data. For two-component data sample the idea is to break the sample in two parts and permute one of the components in the second part, while leaving the first part intact. The resulting two samples are then jointly ranked and a data compressor is applied to the resulting (binary) data string. The components are deemed independent if the string cannot be compressed. This procedure gives a provably valid test against all possible alternatives (that is, the test is distribution-free) provided the data compressor was ideal.