AI 中文总结
针对高光谱图像分类，传统张量分解方法有局限，本文引入整体多变量分解框架，通过新张量算法建模多种交互，经实验验证其在多数据集及算法下分类准确率更高，是解决高光谱复杂数据结构的有效框架。
AI 中文摘要
张量分解是高光谱图像分类中特征提取的基础工具，传统上由塔克分解和典范多adic分解主导。但这些方法难以完全封装多维高光谱数据中深度耦合的几何和内在结构，忽略了复杂的跨域空间和光谱相互依赖性。为此引入整体多变量分解框架，其提供了一种新颖、结构灵活的张量算法，能明确建模孤立的空间和光谱行为以及复杂的高维协作交互。在四个基准高光谱数据集上的综合实验表明，与传统方法相比，该方法在不同监督学习算法下能产生更高的分类准确率，是解决高光谱分析中复杂多维数据结构的强大计算框架。
英文摘要
Tensor decomposition serves as a foundational tool for feature extraction in hyperspectral image classification, a domain classically dominated by the Tucker and Canonical Polyadic decompositions. Although widely adopted, these schemes often struggle to fully encapsulate the deeply coupled geometric and intrinsic structures inherent to multidimensional hyperspectral data. Their structural reliance on rigid low-rank approximations successfully captures independent mode variations but systematically neglects complex, cross-domain spatial and spectral interdependencies. To overcome this limitation, we introduce the Holistic Multivariance Decomposition framework to achieve highly discriminative hyperspectral feature extraction. The Holistic Multivariance Decomposition provides a novel, structurally flexible tensor algorithm that explicitly models isolated spatial and spectral behaviors, alongside intricate, higher dimensional cooperative interactions. Comprehensive experimental evaluations across four benchmark hyperspectral datasets demonstrate that the proposed Holistic Multivariance Decomposition approximants consistently yield superior classification accuracy compared to conventional Tucker and Canonical Polyadic decomposition methods across diverse supervised learning algorithms. By effectively preserving essential joint multivariance features even under severe subspace compression, these results establish the Holistic Multivariance Decomposition as a robust, high fidelity computational framework for resolving complex multidimensional data structures in hyperspectral analysis.
