Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

41

ISNN
2005
Springer

favoriteEmaildiscussreport

128views Neural Networks» more ISNN 2005»

A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

14 years 4 months ago

A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

Download www.comp.hkbu.edu.hk

This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without timeconsuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.

Hailin Liu, Yiu-ming Cheung

Real-time Traffic

Blind Source Separation | ISNN 2005 | Typical Information-theoretical Approaches | Unknown Unmixing Matrix |

claim paper

Related Content

» Maximum Likelihood Blind Source Separation A ContextSensitive Generalization of ICA

» On Optimal Selection of Correlation Matrices for MatrixPencilBased Separation

» A null space method for overcomplete blind source separation

» Glimpsing IVA A Framework for OvercompleteCompleteUndercomplete Convolutive Source Separat...

» Finite sample effects of the fast ICA algorithm

» A new mutual information measure for independent component alalysis

» MABLE a framework for learning from natural instruction

» cdec A Decoder Alignment and Learning Framework for FiniteState and ContextFree Translatio...

Post Info
More Details (n/a)

Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	ISNN
Authors	Hailin Liu, Yiu-ming Cheung

Comments (0)