Discrete spectral clustering directly obtains the discrete labels of data, but existing clustering methods assume that the real-valued indicator matrices of different views are identical, which is unreasonable in practical applications. Moreover, they do not effectively exploit the spatial structure and complementary information embedded in views. To overcome this disadvantage, we propose a tensorized discrete multi-view spectral clustering model that integrates spectral embedding and spectral rotation into a unified framework. Specifically, we leverage the weighted tensor nuclear-norm regularizer on the third-order tensor, which consists of the real-valued indicator matrices of views, to exploit the complementary information embedded in the indicator matrices of different views. Furthermore, we present an adaptively weighted scheme that takes into account the relationship between views for clustering. Finally, discrete labels are obtained by spectral rotation. Experiments show the effectiveness of our proposed method.

Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.

Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning. Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques. For now, however, there is no practical training platform that can help radiologists learn automated medical image analysis systematically. Application Design: The platform proposes the basic learning, model combat, business application (BMR) concept, including the learning guidance system and the assessment training system, which constitutes a closed-loop learning guidance mode of “learning-assessment-training-learning”. Findings: The survey results show that most of radiologists met their learning expectations by using this platform. The platform can help radiologists master deep learning techniques quickly, comprehensively and firmly.

Multiview clustering aims to improve clustering performance by exploring multiple representations of data and has become an important research direction. Meanwhile, graph-based methods have been extensively studied and have shown promising performance in multiview clustering tasks. However, most existing graph-based multiview clustering methods rely on assigning appropriate weights to each view based on its importance, with the clustering results depending on these weight assignments. In this paper, we propose an a novel multiview spectral clustering framework with reduced computational complexity that captures complementary information across views by optimizing a global-view graph using adaptive weight learning. Additionally, in our method, once the Global-view Graph is obtained, cluster labels can be directly assigned to each data point without the need for any post-processing, such as the K-means required in standard spectral clustering. Our method not only improves clustering performance but also reduces computational resource consumption. Experimental results on real-world datasets demonstrate the effectiveness of our approach.

Anchor graphs are widely recognized for their capability to reduce the computational complexity of clustering. When regression techniques are applied to anchor graphs, existing regression-based methods mainly focus on directly learning sample labels, while the label distribution induced on anchors through sample–anchor relationships remains insufficiently explored. We propose Efficient Discrete Multi-View Clustering with Tensorized Label Learning (EDMVC_TLL) to address this limitation. Specifically, we fully leverage the probabilistic properties of anchor graphs to establish the relationship between sample labels and anchor labels, utilizing anchor labels to guide the learning of sample labels. Furthermore, we introduce an innovative regularization term to balance anchor labels, avoiding trivial solutions and enhancing sample labels’ quality. The tensor Schatten p-norm explores complementary information embedded in different views. Additionally, we adopt an adaptive anchor graph, integrating it into the subsequent optimization process to improve the quality of the anchor graph. An efficient optimization algorithm is developed, and extensive experiments on six benchmark datasets demonstrate that EDMVC_TLL consistently achieves the best performance in terms of ACC, NMI, and Purity among the compared methods.