Loss Function For Blind Source Separation-minimum Entropy Criterion And Its Generalized Anti-Hebbian Rules
Abstract
Blind source separation has been intriguing many scientists. In adaptive signal processing, LMS (Least-mean squared) algorithm has long been used in signal enhancement and noise cancellation but it cannot overcome the difficulty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a statistical modeling of the signals using statistical measures such as a loss function, which is different from the mutual information. This paper will propose a new loss function based on generalized Gaussian distribution family and derive new simple adaptive learning rules. Our separator based on the new generalized `anti-Hebbian rules' is also justified by the simulation on both artificial and real data with good performance.
Recommended Citation
H. C. Wu et al., "Loss Function For Blind Source Separation-minimum Entropy Criterion And Its Generalized Anti-Hebbian Rules," Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 910 - 915, Institute of Electrical and Electronics Engineers, Jan 1999.
Department(s)
Electrical and Computer Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 1999
