# DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

arXiv: https://arxiv.org/abs/1407.8289 · HTML: https://mlsanity.com/paper/1407.8289/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2014.

## Built on by (most cited first, 11 of 11)

- [Tensor Methods in Computer Vision and Deep Learning](https://mlsanity.com/paper/2107.03436/) (2021)
- [Stochastic tensor space feature theory with applications to robust machine learning](https://mlsanity.com/paper/2110.01729/) (2021)
- [Low-Rank Tensor Learning by Generalized Nonconvex Regularization](https://mlsanity.com/paper/2410.18402/) (2024)
- [Generalized Least Squares Kernelized Tensor Factorization](https://mlsanity.com/paper/2412.07041/) (2024)
- [TEC: Tensor Ensemble Classifier for Big Data](https://mlsanity.com/paper/2103.00025/) (2021)
- [MLCTR: A Fast Scalable Coupled Tensor Completion Based on Multi-Layer Non-Linear Matrix Factorization](https://mlsanity.com/paper/2109.01773/) (2021)
- [Structure-Preserving Graph Kernel for Brain Network Classification](https://mlsanity.com/paper/2111.10803/) (2021)
- [Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data](https://mlsanity.com/paper/2201.07683/) (2022)
- [A weighted subspace exponential kernel for support tensor machines](https://mlsanity.com/paper/2302.08134/) (2023)
- [Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decomposition](https://mlsanity.com/paper/2509.14577/) (2025)
- [Subspace Kernel Learning on Tensor Sequences](https://mlsanity.com/paper/2603.19546/) (2026)

## Abstract (arXiv)

With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.