Whitening-Free Least-Squares Non-Gaussian Component Analysis
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| Authors | Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| Journal/Conference Name | Journal of Machine Learning Research |
| Paper Category | Artificial Intelligence |
| Paper Abstract | Non-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 projection pursuit (PP) and independent 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 least-squares NGCA (LSNGCA) has been developed, which gives a solution analytically through least-squares estimation of log-density gradients and eigendecomposition. However, since pre-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 whitening-free LSNGCA method and experimentally demonstrate its superiority. |
| Date of publication | 2016 |
| Code Programming Language | Matlab |
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