Knowledge-Driven and Low-Rank Tensor Regularized Multiview Fuzzy Clustering for Alzheimer’s Diagnosis

Abstract

Alzheimer’s disease (AD), as a complex neurodegenerative disorder, is the most common cause of dementia. In recent years, the emergence of multiview data has brought new possibilities for the diagnosis of AD. However, due to uneven density and uncertainty in the multiview data, existing algorithms still face challenges in extracting consistent and complementary information across views. To address this issue, a multiview fuzzy clustering algorithm, which integrates high-density knowledge point extraction and low-rank tensor regularization (K-LRT-MFC), is proposed in this paper. First, high-density knowledge point extraction is employed to tackle the issue of uneven density in high-dimensional data, enhancing the stability and accuracy of single-view clustering. Second, low-rank tensor regularization is applied to effectively capture high-order complementary information among multiview data, significantly improving the precision and computational efficiency of multiview clustering. Experimental results on several publicly available AD diagnostic datasets demonstrate that the proposed method outperforms existing approaches in terms of accuracy, sensitivity, and specificity, providing an efficient and accurate solution for early AD diagnosis.