A new concept for separability problems in blind source separation
Neural Computation
In Search of Non-Gaussian Components of a High-Dimensional Distribution
The Journal of Machine Learning Research
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Linear dimension reduction based on the fourth-order cumulant tensor
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Uniqueness of non-gaussian subspace analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Joint low-rank approximation for extracting non-Gaussian subspaces
Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Uniqueness of non-gaussian subspace analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
| Hi-index | 0.00 |
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.