Abstract
In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new method to identify the non-Gaussian subspace. A linear dimension reduction algorithm based on the fourth-order cumulant tensor was proposed in our previous work [4]. Although it works well for sub-Gaussian structures, the performance is not satisfactory for super-Gaussian data due to outliers. To overcome this problem, we construct an alternative by using Hessian of characteristic functions which was applied to (multidimensional) independent component analysis [10,11]. A numerical study demonstrates the validity of our method.
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Kawanabe, M., Theis, F.J. (2006). Estimating Non-Gaussian Subspaces by Characteristic Functions. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_20
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DOI: https://doi.org/10.1007/11679363_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
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