Cyclic-Shift Sparse Kronecker Tensor Classifier for Signal-Region Detection in Neuroimaging
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913844898562048 |
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| author | Huang, Hsin-Hsiung Chen, Yuh-Haur Zhang, Teng |
| author_facet | Huang, Hsin-Hsiung Chen, Yuh-Haur Zhang, Teng |
| contents | This study proposes a cyclic-shift logistic sparse Kronecker product decomposition (SKPD) model for high-dimensional tensor data, enhancing the SKPD framework with a cyclic-shift mechanism for binary classification. The method enables interpretable and scalable analysis of brain MRI data, detecting disease-relevant regions through a structured low-rank factorization. By incorporating a second spatially shifted view of the data, the cyclic-shift logistic SKPD improves robustness to misalignment across subjects, a common challenge in neuroimaging. We provide asymptotic consistency guarantees under a restricted isometry condition adapted to logistic loss. Simulations confirm the model's ability to recover spatial signals under noise and identify optimal patch sizes for factor decomposition. Application to OASIS-1 and ADNI-1 datasets demonstrates that the model achieves strong classification accuracy and localizes estimated coefficients in clinically relevant brain regions, such as the hippocampus. A data-driven slice selection strategy further improves interpretability in 2D projections. The proposed framework offers a principled, interpretable, and computationally efficient tool for neuroimaging-based disease diagnosis, with potential extensions to multi-class settings and more complex transformations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cyclic-Shift Sparse Kronecker Tensor Classifier for Signal-Region Detection in Neuroimaging Huang, Hsin-Hsiung Chen, Yuh-Haur Zhang, Teng Methodology Computation 62H35, 65C60 This study proposes a cyclic-shift logistic sparse Kronecker product decomposition (SKPD) model for high-dimensional tensor data, enhancing the SKPD framework with a cyclic-shift mechanism for binary classification. The method enables interpretable and scalable analysis of brain MRI data, detecting disease-relevant regions through a structured low-rank factorization. By incorporating a second spatially shifted view of the data, the cyclic-shift logistic SKPD improves robustness to misalignment across subjects, a common challenge in neuroimaging. We provide asymptotic consistency guarantees under a restricted isometry condition adapted to logistic loss. Simulations confirm the model's ability to recover spatial signals under noise and identify optimal patch sizes for factor decomposition. Application to OASIS-1 and ADNI-1 datasets demonstrates that the model achieves strong classification accuracy and localizes estimated coefficients in clinically relevant brain regions, such as the hippocampus. A data-driven slice selection strategy further improves interpretability in 2D projections. The proposed framework offers a principled, interpretable, and computationally efficient tool for neuroimaging-based disease diagnosis, with potential extensions to multi-class settings and more complex transformations. |
| title | Cyclic-Shift Sparse Kronecker Tensor Classifier for Signal-Region Detection in Neuroimaging |
| topic | Methodology Computation 62H35, 65C60 |
| url | https://arxiv.org/abs/2505.12113 |