Meeting Banner
Abstract #0878

Performing Sparse Regularization and Dimension Reduction Simultaneously in CCA-Based Data Fusion

Zhengshi Yang1, Xiaowei Zhuang1, Christopher Bird1, Karthik Sreenivasan1, Virendra Mishra1, Sarah J Banks1, and Dietmar Cordes1,2

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of Colorado, Boulder, CO, United States

Principal component analysis is commonly used in data fusion for dimension reduction prior to performing fusion analysis. However, PCA does not address that a large proportion of voxels may be irrelevant to extract joint information in data fusion. We implemented sparse PCA to suppress irrelevant voxels while simultaneously reducing the data dimension. Results show that introducing sparsity to data fusion provides better group discrimination.

This abstract and the presentation materials are available to members only; a login is required.

Join Here