Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
This work addresses the problem of fMRI classification for healthcare and neuroscience applications, offering an incremental improvement over existing sparse multilinear regression methods by incorporating t-SVD.
The authors tackled the challenge of classifying functional magnetic resonance imaging (fMRI) data, which is high-dimensional and has few samples, by proposing a sparse multilinear regression method called Sturm that uses tensor singular value decomposition and regularization, achieving superior performance in classifying fMRI with a small number of voxels across four classification problems.
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available. Recent sparse multilinear regression methods based on tensor are emerging as promising solutions for fMRI, yet existing works rely on unfolding/folding operations and a tensor rank relaxation with limited tightness. The newly proposed tensor singular value decomposition (t-SVD) sheds light on new directions. In this work, we study t-SVD for sparse multilinear regression and propose a Sparse tubal-regularized multilinear regression (Sturm) method for fMRI. Specifically, the Sturm model performs multilinear regression with two regularization terms: a tubal tensor nuclear norm based on t-SVD and a standard L1 norm. We further derive the algorithm under the alternating direction method of multipliers framework. We perform experiments on four classification problems, including both resting-state fMRI for disease diagnosis and task-based fMRI for neural decoding. The results show the superior performance of Sturm in classifying fMRI using just a small number of voxels.