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.
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