Hiroaki Shiino,
Hiroaki Sasaki,
Gang Niu,
Masashi Sugiyama
;
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:375-390, 2017.
Abstract
\emphNon-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian “signals” from high-dimensional data contaminated with Gaussian noise. NGCA can be regarded as a generalization of \emphprojection pursuit (PP) and \emphindependent component analysis (ICA) to multi-dimensional and dependent non-Gaussian components. Indeed, seminal approaches to NGCA are based on PP and ICA. Recently, a novel NGCA approach called \emphleast-squares NGCA (LSNGCA) has been developed, which gives a solution analytically through least-squares estimation of \emphlog-density gradients and eigendecomposition. However, since \emphpre-whitening of data is involved in LSNGCA, it performs unreliably when the data covariance matrix is ill-conditioned, which is often the case in high-dimensional data analysis. In this paper, we propose a \emphwhitening-free variant of LSNGCA and experimentally demonstrate its superiority.
@InProceedings{pmlr-v77-shiino17a,
title = {Whitening-Free Least-Squares Non-Gaussian Component Analysis},
author = {Hiroaki Shiino and Hiroaki Sasaki and Gang Niu and Masashi Sugiyama},
booktitle = {Proceedings of the Ninth Asian Conference on Machine Learning},
pages = {375--390},
year = {2017},
editor = {Min-Ling Zhang and Yung-Kyun Noh},
volume = {77},
series = {Proceedings of Machine Learning Research},
address = {},
month = {15--17 Nov},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v77/shiino17a/shiino17a.pdf},
url = {http://proceedings.mlr.press/v77/shiino17a.html},
abstract = {\emphNon-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian “signals” from high-dimensional data contaminated with Gaussian noise. NGCA can be regarded as a generalization of \emphprojection pursuit (PP) and \emphindependent component analysis (ICA) to multi-dimensional and dependent non-Gaussian components. Indeed, seminal approaches to NGCA are based on PP and ICA. Recently, a novel NGCA approach called \emphleast-squares NGCA (LSNGCA) has been developed, which gives a solution analytically through least-squares estimation of \emphlog-density gradients and eigendecomposition. However, since \emphpre-whitening of data is involved in LSNGCA, it performs unreliably when the data covariance matrix is ill-conditioned, which is often the case in high-dimensional data analysis. In this paper, we propose a \emphwhitening-free variant of LSNGCA and experimentally demonstrate its superiority.}
}
%0 Conference Paper
%T Whitening-Free Least-Squares Non-Gaussian Component Analysis
%A Hiroaki Shiino
%A Hiroaki Sasaki
%A Gang Niu
%A Masashi Sugiyama
%B Proceedings of the Ninth Asian Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2017
%E Min-Ling Zhang
%E Yung-Kyun Noh
%F pmlr-v77-shiino17a
%I PMLR
%J Proceedings of Machine Learning Research
%P 375--390
%U http://proceedings.mlr.press
%V 77
%W PMLR
%X \emphNon-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian “signals” from high-dimensional data contaminated with Gaussian noise. NGCA can be regarded as a generalization of \emphprojection pursuit (PP) and \emphindependent component analysis (ICA) to multi-dimensional and dependent non-Gaussian components. Indeed, seminal approaches to NGCA are based on PP and ICA. Recently, a novel NGCA approach called \emphleast-squares NGCA (LSNGCA) has been developed, which gives a solution analytically through least-squares estimation of \emphlog-density gradients and eigendecomposition. However, since \emphpre-whitening of data is involved in LSNGCA, it performs unreliably when the data covariance matrix is ill-conditioned, which is often the case in high-dimensional data analysis. In this paper, we propose a \emphwhitening-free variant of LSNGCA and experimentally demonstrate its superiority.
Shiino, H., Sasaki, H., Niu, G. & Sugiyama, M.. (2017). Whitening-Free Least-Squares Non-Gaussian Component Analysis. Proceedings of the Ninth Asian Conference on Machine Learning, in PMLR 77:375-390
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