Low Rank Support Quaternion Matrix Machine

• URL: http://arxiv.org/abs/2512.08327v1
• Date: Tue, 09 Dec 2025 07:42:33 GMT
• Title: Low Rank Support Quaternion Matrix Machine
• Authors: Wang Chen, Ziyan Luo, Shuangyue Wang,
• Abstract summary: We propose a novel classification method for color image classification, named as the Low-rank Support Quaternion Matrix Machine (LSQMM)<n>For the purpose of promoting low-rank structures resulting from strongly correlated color channels, a quaternion nuclear norm regularization term is added to the hinge loss in our LSQMM model.<n> Experimental results on multiple color image classification datasets demonstrate that our proposed classification approach exhibits advantages in classification accuracy, robustness and computational efficiency.
• Score: 10.339358618129303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Input features are conventionally represented as vectors, matrices, or third order tensors in the real field, for color image classification. Inspired by the success of quaternion data modeling for color images in image recovery and denoising tasks, we propose a novel classification method for color image classification, named as the Low-rank Support Quaternion Matrix Machine (LSQMM), in which the RGB channels are treated as pure quaternions to effectively preserve the intrinsic coupling relationships among channels via the quaternion algebra. For the purpose of promoting low-rank structures resulting from strongly correlated color channels, a quaternion nuclear norm regularization term, serving as a natural extension of the conventional matrix nuclear norm to the quaternion domain, is added to the hinge loss in our LSQMM model. An Alternating Direction Method of Multipliers (ADMM)-based iterative algorithm is designed to effectively resolve the proposed quaternion optimization model. Experimental results on multiple color image classification datasets demonstrate that our proposed classification approach exhibits advantages in classification accuracy, robustness and computational efficiency, compared to several state-of-the-art methods using support vector machines, support matrix machines, and support tensor machines.

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