Applying PCA neural models for the blind separation of signals

Authors:
Konstantinos I. Diamantaras;Theophilos Papadimitriou
Affiliations:
Technological Education Institute of Thessaloniki, Department of Informatics, 57400 Sindos, Greece;Democritus University of Thrace, Int. Economic Relations and Development Department, 69100 Komotini, Greece
Venue:
Neurocomputing
Year:
2009

Citing 15
Cited 2

Principal component learning networks and applications

Principal component learning networks and applications
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Linear geometric ICA: fundamentals and algorithms

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind Deconvolution of Multi-Input Single-Output Systems With Binary Sources

IEEE Transactions on Signal Processing
Globally convergent blind source separation based on a multiuser kurtosis maximization criterion

IEEE Transactions on Signal Processing
QR factorization based blind channel identification withsecond-order statistics

IEEE Transactions on Signal Processing
Bilinear approach to multiuser second-order statistics-based blindchannel estimation

IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
MIMO blind second-order equalization method and conjugatecyclostationarity

IEEE Transactions on Signal Processing
Blind two-input-two-output FIR channel identification based onfrequency domain second-order statistics

IEEE Transactions on Signal Processing
Adaptive Principal component EXtraction (APEX) and applications

IEEE Transactions on Signal Processing
Waveform-preserving blind estimation of multiple independentsources

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis

IEEE Transactions on Signal Processing

Facial expressions analysis based on cooperative neuro-computing interactions

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Wind energy prediction and monitoring with neural computation

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Principal component analysis is often thought of as a preprocessing step for blind source separation (BSS). Although second order methods have been proposed for BSS in the past, these approaches cannot be easily implemented by neural models. In this paper we demonstrate that PCA is more than a preprocessing step and, in fact, it can be used directly for solving the BSS problem in combination with very simple temporal filtering process. We also demonstrate that a PCA extension called oriented PCA (OPCA) can be also used for the same purpose without prewhitening the observed data. Both approaches can be implemented using efficient neural models that are shown to successfully extract the hidden sources.