Least-Squares Independence Test (LSIT)


Description

Least-Squares Independence Test (LSIT) is a method of testing the null hypothesis that paired (input-output) samples are independent. LSIT adopts a squared-loss variant of mutual information as an independence measure and estimates it using the density-ratio estimation method uLSIF. Thanks to this formulation, all tuning parameters such as the Gaussian width and the regularization parameter can be automatically chosen based on a cross-validation method.


Download

MATLAB implementation of LSIT: LSIT.tgz ("demo_LSIT.m" is the first file to execute).


Examples

LSIT-data1 LSIT-data2 LSIT-data3 LSIT-data4

LSIT-SMI1 LSIT-SMI2 LSIT-SMI3 LSIT-SMI4


References

Sugiyama, M. & Suzuki, T.
Least-squares independence test.
IEICE Transactions on Information and Systems, vol.E94-D, no.6, pp.1333-1336, 2011.
[ paper ]
Masashi Sugiyama (sugi [at] cs.titech.ac.jp)

Sugiyama Laboratory, Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology,
2-12-1-W8-74, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan.
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