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.

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach.

Dictionary learning is an efficient knowledge representation method that can learn the essential features of data. Traditional dictionary learning methods are difficult to obtain nonlinear information when processing large-scale and high-dimensional datasets. While most dictionary learning algorithms are based on the assumption that the training data and test data have the same feature distribution, which is not always true in practical applications. To address the above problems, we propose the Kernel Fisher Dictionary Transfer Learning (KFDTL) algorithm. First, we map each sample to high-dimensional space through kernel mapping and use any dictionary learning algorithm to learn the essential features. Then, the feature-based transfer learning method is performed to predict the labels of the target samples. This method includes three main contributions: (1) KFDTL constructs a discriminative Fisher embedding model to make the same class samples have similar coding coefficients; (2) Based on the relationship between profiles and atoms, KFDTL constructs an adaptive model that adapts source domain samples to target domain samples; (3) The kernel method is used to efficiently solve nonlinear problems. Experiments on a large number of public image datasets have proved the effectiveness of the proposed method. The source code of the proposed method is available at https://github.com/zzfan3/KFDTL .

The existence of a speckle noise significantly affects the accuracy of land-cover change detection results for synthetic aperture radar (SAR) images. This letter proposes a biobjective fuzzy local information clustering method with decomposition (BIFLICM/D) to address this problem. SAR images change detection is described as a biobjective fuzzy local information clustering problem from the aspects of preserving image details and removing noise. To improve the ability to extract the original information detail, the log-mean ratio method is used to generate a first difference image in BIFLICM/D. The second difference image is achieved by combining the homomorphic filtering and saliency detection, which effectively removes the speckle noise. Fuzzy clustering objective functions are then constructed for the two difference images to recognize the changed and unchanged pixels under different requirements. A new fuzzy membership degree-updating method is adopted to optimize the two objective functions, which can balance the influences of the two objectives and improve the robustness of the proposed method. The experimental result demonstrates that the proposed method is more effective and superior to the comparison algorithms.