Abstract
Owing to prominence as a diagnostic tool for probing the
neural correlates of cognition, neuroimaging tensor data
has been the focus of intense investigation. Although many
supervised tensor learning approaches have been proposed,
they either cannot capture the nonlinear relationships of
tensor data or cannot preserve the complex multi-way structural information. In this paper, we propose a Multi-way
Multi-level Kernel (MMK) model that can extract discriminative, nonlinear and structural preserving representations
of tensor data. Specifically, we introduce a kernelized
CP tensor factorization technique, which is equivalent to
performing the low-rank tensor factorization in a possibly
much higher dimensional space that is implicitly defined by
the kernel function. We further employ a multi-way nonlinear feature mapping to derive the dual structural preserving
kernels, which are used in conjunction with kernel machines
(e.g., SVM). Extensive experiments on real-world neuroimages demonstrate that the proposed MMK method can effectively boost the classification performance on diverse brain
disorders (i.e., Alzheimer’s disease, ADHD, and HIV)