Results

For the artificial data sets where ground truth is available, we
follow
[Lange et al., 1999] and report the quality of source identification over a
range of possible threshold levels in the form of ROC curves, i.e. as
a plot of the false positive rate versus the true positive rate at
different threshold levels. We report the temporal accuracy as the
(normalised) correlation between the estimated time course and the
'true' timecourse after projection into the same signal
space as defined by the PPCA decomposition, i.e. correlation
between the estimated time course
and
**, where and index the corresponding
columns in the estimated mixing matrix
and the true
mixing matrix
**** and where
**** is the matrix of the major
eigenvectors of the data covariance. By calculating the temporal
correlation with respect to projected rather than the 'true'
original time courses we ensure that the measure of temporal
accuracy is unconfounded by the dimensionality reduction itself.
**

- Artificial signal in synthetic noise
- Spatio-temporal accuracy of PICA
- Accuracy and dimensionality
- Real FMRI data