#
Least-Squares Dimensionality Reduction (LSDR)

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Description

*Least-Squares Dimensionality Reduction (LSDR)*
is a supervised dimensionality reduction method.
LSDR 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.
Then LSDR maximizes this independence measure
(making the complementary features conditional independent of outputs)
by a natural gradient algorithm.

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Download

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

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Examples

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References

Suzuki, T. & Sugiyama, M.

Sufficient dimension reduction via squared-loss mutual information estimation.

*Neural Computation*,
vol.25, no.3, pp.725-758, 2013.

[
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.

TEL & FAX: +81-3-5734-2699