Existing deep multi-view clustering methods have demonstrated excellent performance, which addressing issues such as missing views and view noise. But almost all existing methods are within a static framework, which assumes that all views have already been collected. However, in practical scenarios, new views are continuously collected over time, which forms the stream of views. Additionally, there exists the data imbalance of quality and distribution between different view streams, i.e., concept drift problem. To this end, we propose a novel Deep Streaming View Clustering (DSVC) method, which mitigates the impact of concept drift on streaming view clustering. Specifically, DSVC consists of a knowledge base and three core modules. Through the knowledge aggregation learning module, DSVC extracts representative features and prototype knowledge from the new view. Subsequently, the distribution consistency learning module aligns the prototype knowledge from the current view with the historical knowledge distribution to mitigate the impact of concept drift. Then, the knowledge guidance learning module leverages the prototype knowledge to guide the data distribution and enhance the clustering structure. Finally, the prototype knowledge from the current view is updated in the knowledge base to guide the learning of subsequent views. Extensive experiments demonstrate that, even in dynamic environments, the clustering performance of DSVC outperforms 12 state-of-the-art DMVC methods under static frameworks.

As information acquisition diversifies, data is acquired and stored in increasing modalities. However, sensor failures or equipment issues can lead to partial data loss in certain views, resulting in incomplete multi-view clustering (IMVC) problems. Although some prototype-based IMVC methods have achieved satisfactory performance, almost all of these methods implicitly assume that the cross-view prototypes are aligned. However, during the generation or selection of prototypes, different networks could produce different prototypes, thereby leading to potential misalignment of prototypes across views, i.e., prototype-unaligned problem (PUP). The presence of PUP could lead to overfitting the model. Additionally, when recovering the missing data, there is uncertainty in data quality under different missing rates, which could lead to the performance instability problem (PIP). To address these issues, we propose Prototype Matching Learning for Incomplete Multi-view Clustering (PMIMC). Specifically, PMIMC leverages relational consistency learning to mitigate the heterogeneity of multi-view data. Subsequently, we design a robust prototype contrastive learning loss for the generated prototypes to reduce the effects of PUP. Finally, we propose a prototype-based imputation strategy, that aims to alleviate the instability of imputation under high missing rates. Extensive experiments demonstrate that PMIMC outperforms 13 state-of-the-art methods in terms of clustering performance and robustness. The code is available at: https: //github.com/hl-yuan/PMIMC.

Multi-view clustering (MVC) can fuse the information of multiple views for robust clustering result, among it two fusion strategies,early-fusionandlate-fusionare widely adopted. Although they have derived many MVC methods, there are still two crucial questions: (1)early-fusionforces multiple views to share a consensus latent representation, which compounds the challenge of excavating view-specific diverse local information; (2)late-fusiongenerates view-partitions independently and then integrates them in the following clustering procedure, where the two procedures cannot guide each other and lack necessary negotiation. In view of this, we propose a novel Graph Proxy Fusion (GPF) method to preserve and fuse view-specific local information concertedly in one unified framework. Specifically, we first propose anchor-based local information learning to capture view-specific local structural information in bipartite graphs; meanwhile, a view-consensus graph learned through self-expressiveness-based proxy graph learning module is deemed as a higher-order proxy; following, the novel graph proxy fusion module integrally embeds all lower-order bipartite graphs in the higher-order proxy via higher-order correlation theory. As a novel fusion strategy, the proposed GPF efficiently investigates the valuable consensus and diverse information of multiple views. Experiments on various multi-view datasets demonstrate the superiority of our method.

Multiview clustering (MVC) can achieve more accurate results by utilizing complementary information from multiple perspectives, compared to traditional single-view methods. However, current multiview techniques require all views to be available upfront, making them inadequate for dealing with prevalent data sources that arrive as streams, such as stem cell analysis and multicamera surveillance. To address this problem, in this article, we propose a method called lifelong stream-view clustering (LSVC), which comprises an embedding anchor knowledge library and three key components, enabling the capability to perform asynchronous clustering on stream views. These three components are specifically: 1) the knowledge extraction module that extracts the abstract knowledge of the newcome view over time and updates the shared knowledge library; 2) the knowledge transfer module that aligns the newcome view with the historical knowledge library, enabling the transfer of structure information to the knowledge library; and 3) the knowledge rule module that constraints the knowledge library to enjoy a fair amount of anchors for each cluster, improving the discrimination of knowledge. The experimental results show that LSVC outperforms traditional single-view clustering (SVC) and MVC methods as it gradually improves with the accumulation of stream views and tends to be stable over time.

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a “three-stage” scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the “one-stage” scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to$O(n)$and$O(n^2)$, respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge a certain extent. However, the unsupervised dynamic strategy usually cannot obtain the optimal anchors for MVC. The main reasons are that it does not consider the fairness of different views and lacks the priori supervised guidance. To completely solve these problems, we first propose the priori anchor graph regularization (PAGG) for scalable multi-view bipartite graph clustering, dubbed as SMGC method. Specifically, SMGC learns a few representative consensus anchors to simulate the numerous view data well, and constructs a bipartite graph to bridge the affinities between the anchors and original data points. In order to largely improve the quality of anchors, PAGG predefines prior anchor labels to constrain the anchors with discriminative cluster structure and fair view allocation, such that a better bipartite graph can be obtained for fast clustering. Experimentally, abundant of experiments are accomplished on six scalable benchmark datasets, and the experimental results fully demonstrate the effectiveness and efficiency of our SMGC.

Graph-based clustering has become an active topic due to the efficiency in characterizing the relationships between the samples via graph. To improve the quality of graph, recent works propose to utilize global and local information. However, existing methods may lead to a degenerated graph when facing noisy and uneven distributed data. Since 1) they preserve the local information by referring the similarity between each sample-pair, whose confidence is easily disturbed by the poor quality samples; and 2) although the global information is relatively robust to the noisy, existing methods have island effect that lies between local and global structures learning, such that the information of both can not be utilized mutually. To alleviate these issues, this paper presents explicit local coupling global structure clustering (ELGSC) to explicitly learn the local structure and global structure information via a coupling scheme. To be specific, we learn$l(\ll n)$pseudo samples as the anchors to reflect local hot spots distribution, where$n$is the number of samples. By referring the relationship between each anchor-sample pair, ELGSC is capable of obtaining an effective local bipartite graph to capture the local structure. Meanwhile, the self-expressiveness learning is adopted to pursue a lower-rank global affinity graph. Finally, a higher-order coupling learning framework is proposed to couple the learning of global affinity graph and local bipartite graph. Thus, local and global structure information could be propagated each other on both graphs. The experimental results on real datasets demonstrate the efficacy of the proposed method over state-of-the-arts.