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ABSTRACT: Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval classification rates increase if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised classifier which works from unsupervised ICA features is invoked. In addition, we demonstrate the suggested framework for automatic annotation of descriptive key words to images.
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on; 02/2002
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ABSTRACT: In certain applications of independent component analysis (ICA) it
is of interest to test hypotheses concerning the number of components or
simply to test whether a given number of components is significant
relative to a "white noise" null hypothesis. We estimate probabilities
of such competing hypotheses for ICA based on dynamic decorrelation. The
probabilities are evaluated in the so-called Bayesian information
criterion approximation, however, they are able to detect the content of
dynamic components as efficiently as an unbiased test set
estimator
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on; 02/2001 · 4.63 Impact Factor
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ABSTRACT: We discuss the application of ICA procedures to fMRI (functional Magnetic Resonance Imaging) sequences. While principal component analysis can identify activation patterns that are uncorrelated in both space and time ICA can identify events that are independent in either time or space. We show that the activation related components found by either spatial or temporal independency are consistent, hence robust to choice of spatial or temporal separation and to choice of ICA approach. We discuss these issues in the context of three ICA algorithms applied to an fMRI visual activation study.
09/2000;
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ABSTRACT: We apply and discuss generalizable Gaussian mixture (GGM) models
for text mining. The model automatically adapts model complexity for a
given text representation. We show that the generalizability of these
models depends on the dimensionality of the representation and the
sample size. We discuss the relation between supervised and unsupervised
learning in the test data. Finally, we implement a novelty detector
based on the density model
Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on; 02/2000 · 4.63 Impact Factor
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ABSTRACT: We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain-behavior relationships that may remain hidden if only a single method is employed.
NeuroImage 10/1999; 10(3 Pt 1):282-303. · 5.89 Impact Factor
