Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
HOS-based generalized noise pdf models for signal detection optimization
Signal Processing
Natural gradient works efficiently in learning
Neural Computation
High-order contrasts for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Kernel independent component analysis
The Journal of Machine Learning Research
A robust approach to independent component analysis of signals with high-level noise measurements
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The purpose of this paper is to develop two novel unified parametric and non-parametric Independent Component Analysis (ICA) algorithms, which enable to separate arbitrary sources including symmetric and asymmetric distributions with self-adaptive score functions. They are derived from the parameterized asymmetric generalized Gaussian density (AGGD) model and GGD kernel based k-nearest neighbor (KNN) non-parametric estimation. The parameters of the score function in the algorithms are been chosen adaptively by estimating the high order statistics of the observed signals and GGD kernel estimation based non-parametric method. Compared with conventional ICA algorithms, the two given methods can separate a wide range of source signals using only one unified density model. Simulations confirm the effectiveness and performance of the proposed algorithm.