Icasso: software for investigating the reliability of ICA estimates by clustering and visualization
A major problem in application of independent component analysis (ICA)
is that the reliability of the estimated independent components is not
known. Firstly, the finite sample size induces statistical errors in
the estimation. Secondly, as real data never exactly follows the ICA
model, the contrast function used in the estimation may have many
local minima which are all equally good, or the practical algorithm
may not always perform properly, for example getting stuck in local
minima with strongly suboptimal values of the contrast function.
present an explorative visualization method for investigating the
relations between estimates from FastICA. The algorithmic and
statistical reliability/stability is investigated by running the algorithm many
times with different initial values or with differently bootstrapped
data sets, respectively.
Reliable estimates correspond to tight clusters, and
unreliable ones to points which do not belong to any such cluster. We
have developed a software package called Icasso to implement
these operations for assessment of ICA estimates.
Oct 20, 2005: v1.22
A bug appearing in the new Matlab7 has been corrected more >>>
Mar 07, 2005: v1.21
A bug in computing the 'R-index' is corrected. Input syntax changes in some subfunctions more >>>
Feb 13, 2005: The new, completely revised Icasso 1.2
Documentation and references:
Icasso 1.21 software package runs under MATLAB
. At least version
6.1 is required
. It needs also the FastICA 2
Read about Icasso (copyright notice) and download the software.
If you have any comments or bug reports on the package, contact Aapo Hyvärinen
Page maintained by email@example.com,
last updated Wednesday, 26-Oct-2005 16:34:42 EEST