%?RobustICA?algorithm?for?independent?component?analysis?（Release?1?-?March?31?2008）
%?-----------------------------------------------------------------------------------
%?
%?RobustICA?is?based?on?the?normalized?kurtosis?contrast?function?which?is?optimized?by?a
%?computationally?efficient?gradient-descent?technique.?This?technique?computes?algebraically
%?the?step?size?（adaption?coefficient）?globally?optimizing?the?contrast?in?the?search?direction
%?at?each?iteration.?Any?independent?component?with?non-zero?kurtosis?can?be?extracted?in?this
%?manner.
%
%?The?present?implementation?performs?the?deflationary?separation?of?statistically?independent
%?sources?under?the?instantaneous?linear?mixture?model.?Full?separation?is?achieved?if?at?most
%?one?source?has?zero?kurtosis.
%
%?Some?advantages?of?RobustICA?are:
%
%?-?The?optimal?step-size?technique?provides?some?robustness?to?the?presence?of?saddle?points?and
%???spurious?local?extrema?in?the?contrast?function.
%
%?-?The?method?shows?a?very?high?convergence?speed?measured?in?terms?of?source?extraction?quality
%???versus?number?of?operations.
%
%?-?Real-?and?complex-valued?signals?are?treated?by?exactly?the?same?algorithm.?Both?type?of?source
%???signals?can?be?present?simultaneously?in?a?given?mixture.?Complex?sources?need?not?be?circular.
%???The?mixing?matrix?coefficients?may?be?real?or?complex?regardless?of?the?source?type.
%
%?-?Sequential?extraction?（deflation）?can?be?performed?via?linear?regression.?As?a?result?prewhitening
%???and?the?performance?limitations?it?imposes?can?be?avoided.?This?feature?may?prove?especially
%???beneficial?in?ill-conditioned?scenarios?the?convolutive?case?and?underdetermined?mixtures.
%
%?-?Optionally?the?algorithm?can?target?sub-Gaussian?or?super-Gaussian?sources?in?the?order?defined
%???by?a?kurtosis-sign?vector?provided?by?the?user.?
%
%
%?The?package?is?composed?of?the?following?M-files:
%
%??-?‘robustica.m‘:?????????????implements?the?algorithm?itself.
%
%??-?‘kurt_gradient_optstep.m‘:?computes?the?optimal?step-size?of?the?normalized?kurtosis?contrast
%???????????????????????????????using?the?gradient?vector?as?search?direction.?
%
%??-?‘deflation_regression.m‘:??performs?deflation?via?linear?regression.
%
%??-?‘robustica_demo.m‘:????????a?simple?demonstration?illustrating?the?performance?of?RobustICA
%???????????????????????????????on?synthetic?mixtures.
%?
%
%?More?details?about?the?RobustICA?algorithm?can?be?found?in?the?references?below:
%
%?-?V.?Zarzoso?and?P.?Comon?“Comparative?Speed?Analysis?of?FastICA“?
%???in:?Proceedings?ICA-2007?7th?International?Conference?on?Independent?Component?Analysis
%???and?Signal?Separation?London?UK?September?9-12?200

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----
?????文件????????6909??2008-03-29?10:47??robustica_demo.m
?????文件????????3944??2008-03-31?14:46??contents.m
?????文件????????1827??2008-03-31?12:42??deflation_regression.m
?????文件????????5738??2008-03-31?12:51??kurt_gradient_optstep.m
?????文件????????8748??2008-03-31?12:48??robustica.m