Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data
Qikun Jiang, Jun Shi · 2014
The neuroimaging data typically has extremely high dimensions. Therefore, dimensionality reduction is commonly used to extract discriminative features. Kernel entropy component analysis (KECA) is a newly developed data transformation method, where the key idea is to preserve the most estimated Renyi entropy of the input space data set via a kernel-based estimator. Despite its good performance, KECA still suffers from the problem of low computational efficiency for large-scale data. In this paper, we proposed a sparse KECA (SKECA) algorithm with the recursive divide-and-conquer based solution, and then applied it to perform dimensionality reduction of neuroimaging data for classification of the Alzheimer's disease (AD). We compared the SKECA with KECA, principal component analysis (PCA), kernel PCA (KPCA) and sparse KPCA. The experimental results indicate that the proposed SKECA has most superior performance to all other algorithms when extracting discriminative features from neuroimaging data for AD classification.