Canonical Correlation Analysis for Data Fusion and Group Inferences
Year: 2010
Type: article
Abstract: We have presented two CCA-based approaches for data fusion and group analysis of biomedical imaging data and demonstrated their utility on fMRI, sMRI, and EEG data. The results show that CCA and M-CCA are powerful tools that naturally allow the analysis of multiple data sets. The data fusion and group analysis methods presented are completely data driven, and use simple linear mixing models to decompose the data into their latent components. Since CCA and M-CCA are based on second-order statistics they provide a relatively lessstrained solution as compared to methods based on higherorder statistics such as ICA. While this can be advantageous, the flexibility also tends to lead to solutions that are less sparse than those obtained using assumptions of non-Gaussianity-in particular superGaussianity-at times making the results more difficult to interpret. Thus, it is important to note that both approaches provide complementary perspectives, and hence it is beneficial to study the data using different analysis techniques.
Source: IEEE Signal Processing Magazine
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Unified multivariate data-driven solutions for static and dynamic brain connectivity, Neural Mechanism of Schizophrenia: Use of Multiple Neuroimaging Tools to Examine, Informed Data-Driven Fusion of Behavior, Brain Function, and Genes, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W2081689238