Neural Information Processing - Letters and Reviews
Vol. 6, No. 1, January 2005
pp.1-57
Seungjin Choi
Department of
Computer Science
Pohang University of Science and Technology
San
31, Hyoja-dong, Nam-gu, Pohang, Gyungbuk 790-784, Korea
E-mail:
seungjin@postech.ac.kr
Andrzej Cichocki
RIKEN, Brain
Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Warsaw
University of Technology, Poland
E-mail: cia@bsp.brian.riken.go.jp
Hyung-Min Park and Soo-Young Lee
Department
of BioSystems, Department of Electrical Engineering and Computer
Science, and
CHUNG Moon Soul Center for BioInformation and BioElectronics,
Korea
Advanced Institute of Science and Technology
373-1 Guseong-dong,
Yuseong-gu, Daejeon 305-701, Korea
E-mail: {hmpark, sylee}@kaist.ac.kr
Abstract
Blind source separation (BSS) and
independent component analysis (ICA) are generally based on
a
wide class of unsupervised learning algorithms and they found potential
applications in many areas
from engineering to neuroscience.
A recent trend in BSS is to consider problems in the framework
of
matrix factorization or more general signals decomposition with
probabilistic generative and tree
structured graphical models
and exploit a priori knowledge about true nature and structure of
latent (hidden)
variables or sources such as spatio-temporal
decorrelation, statistical independence, sparseness, smoothness
or
lowest complexity in the sense e.g., of best predictability. The
possible goal of such decomposition can be
considered as the
estimation of sources not necessary statistically independent and
parameters of a mixing
system or more generally as finding a
new reduced or hierarchical and structured representation for the
observed
(sensor) data that can be interpreted as physically
meaningful coding or blind source estimation. The key issue
is
to find a such transformation or coding (linear or nonlinear) which
has true physical meaning and interpretation.
We present a review
of BSS and ICA, including various algorithms for static and dynamic
models and their applications.
The paper mainly consists of three
parts:(1) BSS algorithms for static models (instantaneous mixtures);
(2) extension
of BSS and ICA incorporating with sparseness or
non-negativity constraints; (3) BSS algorithms for dynamic models
(convolutive mixtures).
Keywords ??? Independent
Component Analysis, Blind Source Separation, information theory,
feature extraction