Partial Multi-view Clustering Research Articles

Partial Multi-View Clustering via Self-Supervised Network

Partial multi-view clustering is a challenging and practical research problem for data analysis in real-world applications, due to the potential data missing issue in different views. However, most existing methods have not fully explored the correlation information among various incomplete views. In addition, these existing clustering methods always ignore discovering discriminative features inside the data itself in this unsupervised task. To tackle these challenges, we propose Partial Multi-View Clustering via Self-Supervised \textbf{N}etwork (PVC-SSN) in this paper. Specifically, we employ contrastive learning to obtain a more discriminative and consistent subspace representation, which is guided by a self-supervised module. Self-supervised learning can exploit effective cluster information through the data itself to guide the learning process of clustering tasks. Thus, it can pull together embedding features from the same cluster and push apart these from different clusters. Extensive experiments on several benchmark datasets show that the proposed PVC-SCN method outperforms several state-of-the-art clustering methods.

Self-filling evidential clustering for partial multi-view data

Partial multi-view clustering (PMVC) is a hot topic in data mining, where each view suffers from the absence of some data. Although many PMVC algorithms have been presented with appropriate performance, to the best of our knowledge, almost all of them need to be fed with the number of clusters as hyperparameters. Moreover, these existing algorithms only create hard partitions for objects with multiple views, which are often in highly overlapping areas. This ignores ambiguity and uncertainty in object assignment, which likely leads to performance degradation. To address these issues, we propose a novel self-filling evidential clustering algorithm (SFPMEC) for the PMVC problem. Based on the shared coefficient matrix learned from a self-filling process, SFPMEC provides a human-readable chart through which the number of clusters can be easily detected. SFPMEC then derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering and gaining deeper insight into the data structure. Extensive experiments are conducted to demonstrate the benefits of adopting the self-filling process and evidence theory. Besides, our algorithm shows better performance than other state-of-the-art methods on benchmark datasets11The source code can be found in https://drive.google.com/file/d/1KMi0N8cX85YhwiNjetBwA6QeolQP5TnC/view?usp=sharing..

Graph Contrastive Partial Multi-View Clustering

With the diversity of information acquisition, data is stored and transmitted in an increasing number of modalities. Nevertheless, it is not unusual for parts of the data to be lost in some views due to unavoidable acquisition, transmission or storage errors. In this paper, we propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. Notably, we suppose that the representations of similar samples (i.e., belonging to the same cluster) should be similar. This is distinct from the general unsupervised contrastive learning that assumes an image and its augmentations share a similar representation. Specifically, relation graphs are constructed using the nearest neighbors to identify existing similar samples, then the constructed inter-instance relation graphs are transferred to the missing views to build graphs on the corresponding missing data. Subsequently, two main components, within-view graph contrastive learning and cross-view graph consistency learning, are devised to maximize the mutual information of different views within a cluster. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering. Experiments on several challenging datasets demonstrate the superiority of our proposed methods.

Generative Partial Visual-Tactile Fused Object Clustering

Visual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering performance. However, the missing data (i.e., partial data) issues always happen due to occlusion and noises during the data collecting process. This issue is not well solved by most existing partial multi-view clustering methods for the heterogeneous modality challenge. Naively employing these methods would inevitably induce a negative effect and further hurt the performance. To solve the mentioned challenges, we propose a Generative Partial Visual-Tactile Fused (i.e., GPVTF) framework for object clustering. More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces. A conditional cross-modal clustering generative adversarial network is then developed to synthesize one modality conditioning on the other modality, which can compensate missing samples and align the visual and tactile modalities naturally by adversarial learning. To the end, two pseudo-label based KL-divergence losses are employed to update the corresponding modality-specific encoders. Extensive comparative experiments on three public visual-tactile datasets prove the effectiveness of our method.

Partial multiview clustering with locality graph regularization

Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real-world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi-nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method.

Generative Partial Multi-View Clustering With Adaptive Fusion and Cycle Consistency.

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model with adaptive fusion and cycle consistency, named as GP-MVC, to solve the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies in two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the shared cluster structure across multiple views. Second, view-specific generative adversarial networks with multi-view cycle consistency are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where the learned common representation facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods.

Joint Representation Learning and Clustering: A Framework for Grouping Partial Multiview Data

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and Clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.

Adaptive Anchor-Based Partial Multiview Clustering

Partial multiview clustering, which aims to effectively merge multiple prespecified incomplete views to improve clustering performance, is a research hotspot and difficulty in the field of machine learning. Guo et al . proposed a partial multiview clustering method (APMC) based on anchor graph, which uses a Gauss kernel function to solve the similarity matrix. The Gaussian kernel function is sensitive to parameter $\sigma $ , and it is difficult to find the optimal value only by stepwise adjustment in practical applications. This undoubtedly affects the practicality of the APMC algorithm. To address this issue, an adaptive partial multiview clustering method based on anchor graph (AAPMC) is developed in this paper, which proposes an adaptive neighbor assignment strategy and utilizes it to improve the anchor-based similarity matrix computation of each view. The method proposed in this paper uses an adaptive method to solve the similarity matrix, eliminating the tediousness of parameter adjustment. In addition, the non-iterative method based on anchors is used to solve the optimal solution with low time complexity and is suitable for large-scale datasets. In short, our method is simple and effective, and it is easier to implement in practice. Extensive experiments show that our model can not only effectively solve the parameter setting problem, but also performs better than the state-of-the-art partial multiview clustering methods.

Adaptive Sample-level Graph Combination for Partial Multiview Clustering.

Multiview clustering explores complementary information among distinct views to enhance clustering performance under the assumption that all samples have complete information in all available views. However, this assumption does not hold in many real applications, where the information of some samples in one or more views may be missing, leading to partial multiview clustering problems. In this case, significant performance degeneration is usually observed. A collection of partial multiview clustering algorithms has been proposed to address this issue and most treat all different views equally during clustering. In fact, because different views provide features collected from different angles/feature spaces, they might play different roles in the clustering process. With the diversity of different views considered, in this study, a novel adaptive method is proposed for partial multiview clustering by automatically adjusting the contributions of different views. The samples are divided into complete and incomplete sets, while a joint learning mechanism is established to facilitate the connection between them and thereby improve clustering performance. More specifically, the method is characterized by a joint optimization model comprising two terms. The first term mines the underlying cluster structure from both complete and incomplete samples by adaptively updating their importance in all available views. The second term is designed to group all data with the aid of the cluster structure modeled in the first term. These two terms seamlessly integrate the complementary information among multiple views and enhance the performance of partial multiview clustering. Experimental results on real-world datasets illustrate the effectiveness and efficiency of our proposed method.

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

Partial Multi-View Clustering Based on Sparse Embedding Framework

In this paper, we propose a novel partial multi-view clustering method based on the sparse embedding framework, which can handle incomplete view data well and obtain good clustering performance. Most real-world datasets are often comprised of different views, which provide complementary information for each other for clustering or classification tasks. Existing multi-view clustering methods assume that each example appears in all views or all examples contained at least one view. But each view of real data is often subject to various degrees of damage resulting in partial examples. There are some works on partial view clustering, in which almost all are based on standard non-negative matrix factorization (NMF) to solve the problem that did not consider the sparseness degree of coefficient and unsatisfactory basic matrices learned by NMF. Besides, most existing clustering methods only carry out dimensionality reduction before learning the models, which is unable to make full use of the discriminative information in raw data. Thus, partial multi-view clustering based on sparse embedding framework is proposed, in which dimensionality reduction and dictionary learning are taken into consideration simultaneously to learn better sparse coefficients and dictionaries. The proposed method preserves as much useful information as possible in original space by constraining projection matrix to be orthogonal and imposes Fisher discrimination analysis on dictionaries rather than sparse coefficients, which makes the learned dictionaries more discriminating and promotes sparsity of coefficients. Finally, the synthetic dataset, the extended Yale B dataset, the MNISIT Handwritten Digit dataset and the large dataset-Caltech101 are employed to perform clustering tasks, and the experimental results show that the proposed method achieved a better clustering performance than the other state-of-the-art clustering methods.

Partial multi-view spectral clustering

Abstract The partial multi-view clustering is an emerging hot research area. For example, in web page clustering, the web page content or its linkage information may suffer from the missing of some data. Traditional multi-view clustering methods deal with this kind of problem by completing and clustering separately and thus degrade the clustering performance. In this paper, we propose a new method, named as partial multi-view spectral clustering (PVSC), to cluster partial multi-view data directly. We propose a unified objective function to optimize clustering results of both individual part on each view and shared part among different views, without requiring the full representations of all views. Based on the assumption that an example in multiple views would be assigned to the same cluster with high probability, these shared parts whose examples appear in multiple views have coherent clustering relationships. Meanwhile, the nonnegative and orthogonal constraints are also added to enhance the robustness and efficiency of our methods. Besides, we provide an iterative algorithm for solving our formulated objective. The experimental results on partial multi-view datasets validate the effectiveness of our proposed method

Partial Multi-View Clustering

Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples. In real tasks, however, it is oftenthe case that every view suffers from the missing ofsome data and therefore results in many partial examples,i.e., examples with some views missing. In this paper,we present possibly the first study on partial multiviewclustering. Our proposed approach, PVC, worksby establishing a latent subspace where the instancescorresponding to the same example in different viewsare close to each other, and similar instances (belongingto different examples) in the same view should bewell grouped. Experiments on two-view data demonstratethe advantages of our proposed approach.