Multi-view Clustering Method Research Articles

Esthetic preference mining of Chinese typefaces using multiview cluster analysis

Previous neuroesthetic studies have proved that Chinese typefaces can be viewed as an esthetic preference stimulus by observing differences in event related potential (ERP) waves among three preferences, namely, like, dislike, and neutral. We first reconfirm this conclusion by introducing a multiview clustering method of kernelized tensor singular value decomposition (KT-SVD) to construct an esthetic preference recognition model based on electroencephalograms (EEGs). Our approach regards data from different frequency bands as different views describing the esthetic preferences of Chinese fonts, explore the relevance of all view features through the constraint of tensor multi-rank minimization, and obtains the esthetic preferences using the clustering results. Additionally, the input-perturbation correlation method is used to correlate the amplitude of the electrodes with different types of esthetic preferences and describe the relationship between the key frequency-band combinations and electrodes, and take out the electrodes most relevant to likes, dislikes, and neutrality, including 3 electrodes of Top-1, 6 electrodes of Top-2, 9 electrodes of Top-3, and 12 electrodes of Top-4, forming four different combinations of EEG features for esthetic preference recognition experiments. Experimental results show that the method based on multiview clustering can solve the correlation analysis of neural signals and esthetic preferences and mine the electrodes most relevant to the esthetic preferences of fonts.

Unsupervised Multi-View Clustering by Squeezing Hybrid Knowledge From Cross View and Each View

Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithms still have shortcomings in some aspects, such as removal of redundant information, utilization of various views and fusion of multi-view features. In view of these problems, this paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. We construct two new data matrix decomposition models into a unified optimization model. In this framework, we address the significance of the common knowledge shared by the cross view and the unique knowledge of each view by presenting new low-rank and sparse constraints on the sparse subspace matrix. To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data. Finally, the proposed method is compared with several state-of-the-art algorithms. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.

Multi-View Learning a Decomposable Affinity Matrix via Tensor Self-Representation on Grassmann Manifold

Multi-view clustering aims to partition objects into potential categories by utilizing cross-view information. One of the core issues is to sufficiently leverage different views to learn a latent subspace, within which the clustering task is performed. Recently, it has been shown that representing the multi-view data by a tensor and then learning a latent self-expressive tensor is effective. However, early works mainly focus on learning essential tensor representation from multi-view data and the resulted affinity matrix is considered as a byproduct or is computed by a simple average in Euclidean space, thereby destroying the intrinsic clustering structure. To that end, here we proposed a novel multi-view clustering method to directly learn a well-structured affinity matrix driven by the clustering task on Grassmann manifold. Specifically, we firstly employed a tensor learning model to unify multiple feature spaces into a latent low-rank tensor space. Then each individual view was merged on Grassmann manifold to obtain both an integrative subspace and a consensus affinity matrix, driven by clustering task. The two parts are modeled by a unified objective function and optimized jointly to mine a decomposable affinity matrix. Extensive experiments on eight real-world datasets show that our method achieves superior performances over other popular methods.

Deep Multi-View Subspace Clustering With Unified and Discriminative Learning

Deep multi-view subspace clustering has achieved promising performance compared with other multi-view clustering. However, existing deep multi-view subspace clustering only considers the global structure for all views, and they ignore the local geometric structure among each view. In addition, they cannot learn discriminative feature on different clusters of different views, i.e., inter-cluster difference. To solve these problems, in this paper, we propose a novel Deep Multi-view Subspace Clustering with Unified and Discriminative Learning (DMSC-UDL). DMSC-UDL combines global and local structures with self-expression layer. The global and local structures help each other forward and achieve small distance between samples of the same cluster. To make samples in different clusters of different views farther, DMSC-UDL uses a discriminative constraint between different views. In this way, DMSC-UDL makes the same cluster's samples have large weights, while different clusters’ samples have small weights. Thus, it can learn a better shared connection matrix for multi-view clustering. Extensive experimental results reveal that the proposed multi-view clustering method is superior to several state-of-the-art multi-view clustering methods in terms of performance.

Multi-view clustering via adversarial view embedding and adaptive view fusion

Multi-view clustering, which explores complementarity and consistency among multiple distinct feature sets to boost clustering performance, is becoming more and more useful in many real-world applications. Traditional approaches usually map multiple views to a unified embedding, in which some weighted mechanisms are utilized to measure the importance of each view. The embedding, serving as a clustering friendly representation, is then sent to extra clustering algorithms. However, a unified embedding cannot cover both complementarity and consistency among views and the weighted scheme measuring the importance of each view as a whole ignores the differences of features in each view. Moreover, because of lacking in proper grouping structure constraint imposed on the unified embedding, it will lead to just multi-view representation learned, which is not clustering friendly. In this paper, we propose a novel multi-view clustering method to alleviate the above problems. By dividing the embedding of a view into unified and view-specific vectors explicitly, complementarity and consistency can be reflected. Besides, an adversarial learning process is developed to force the above embeddings to be non-trivial. Then a fusion strategy is automatically learned, which will adaptively adjust weights for all the features in each view. Finally, a Kullback-Liebler (KL) divergence based objective is developed to constrain the fused embedding for clustering friendly representation learning and to conduct clustering. Extensive experiments have been conducted on various datasets, performing better than the state-of-the-art clustering approaches.

Exsavi: Excavating both sample-wise and view-wise relationships to boost multi-view subspace clustering

Multi-view clustering aims to partition objects based on multiple views through unsupervised learning. To exploit cross-view information, recent advances have shifted the focus from matrix-based to tensor-based subspace learning. Though, both approaches leverage the subspace representation of the data, further higher-order statistics extraction is often overlooked. To tackle the issue, in this paper, a novel multi-view clustering method is proposed to extract both intrinsic sample-wise and view-wise statistics from multi-view data. The multi-view data is represented in tensor form and mapped into a latent tensor subspace to exploit its sample-wise relationship. Through using multi-dimensional sparse coding in its similarity tensor, the view-wise relationship is exploited and evacuated. The two relationships are jointly learned and a latent clustering structure is essentially captured in a data-driven way. We conducted extensive experiments on face, object, digital ima ge, and text datasets and compared with ten state-of-the-art methods. The experimental results demonstrated that the proposed method, named as Exsavi, outperform the baselines in terms of various evaluation metrics.

Multi-view clustering by exploring complex mapping relationship between views

Abstract Almost all of the existing methods assume that the samples between different views have a strict one-to-one relationship whether it is for complete multi-view data or for partial multi-view data. In this paper, we refer to the neglected many-to-many relationship between cross-view samples as the complex mapping relationship between views. To address this issue, we propose a resultful Complex Mapping Multi-View Clustering (CMMVC) method by exploring the complex mapping relationship between views. We firstly construct a complex mapping relationship matrix for each pair of views by using the nearest neighbor relationship between cross-view samples. Then the complex mapping relationship matrix is introduced into the framework of multi-view clustering based on non-negative matrix factorization to guide multi-view information fusion in order to obtain more compact representation of multi-view data space. Finally, we give the objective function of CMMVC and an effective optimization scheme. The experimental results demonstrate the advantages of the proposed CMMVC method on multi-view clustering tasks by mining the complex mapping relationship between different views.

Self-weighted Multi-view Fuzzy Clustering

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.

Shared Generative Latent Representation Learning for Multi-View Clustering

Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the various views. Specifically, benefitting from the success of the deep generative learning, the proposed model can not only extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results on several datasets with different scales demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria.

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.

Multi-View Spectral Clustering With Incomplete Graphs

Traditional multi-view learning usually assumes each instance appears in all views. However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing. How to effectively cluster this kind of partial multi-view data has attracted much attention. In this paper, we propose an incomplete multi-view clustering method, namely Multi-view Spectral Clustering with Incomplete Graphs (MSCIG), which connects processes of spectral embedding and similarity matrix completion to achieve better clustering performance. Specifically, MSCIG recovers missing entries of each similarity matrix based on multiplications of a common representation matrix and corresponding view-specific representation matrix, and in turn learns these representation matrices based on the complete similarity matrices. Besides, MSCIG adopts the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -th root integration strategy to incorporate losses of multiple views, which characterizes the contributions of different views. Moreover, we develop an iterative algorithm with proved convergence to solve the resultant problem of MSCIG, which updates the common representation matrix, view-specific representation matrices, similarity matrices, and view weights alternatively. We conduct extensive experiments on 9 benchmark datasets to compare the proposed algorithm with existing state-of-the-art incomplete multi-view clustering methods. Experimental results validate the effectiveness of the proposed algorithm.

Auto-Weighted Incomplete Multi-View Clustering

Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters. Previous studies assume that each data instance appears in all views. However, in real-world applications, it is common that each view may contain some missing data instances, resulting in incomplete multi-view data. To address the incomplete multi-view clustering problem, we will propose an auto-weighted incomplete multi-view clustering method in this paper, which learns a common representation of the instances and an affinity matrix of the learned representation simultaneously in a unified framework. Learning the affinity matrix of the representation guides to learn a more discriminative and compact consensus representation for clustering. Moreover, by considering the impact of the significance of different views, an adaptive weighting strategy is designed to measure the importance of each view. An efficient iterative algorithm is proposed to optimize the objective function. Experimental results on various real-world datasets show that the proposed method can improve the clustering performance in comparison with the state-of-the-art methods in most cases.

Deep graph regularized non-negative matrix factorization for multi-view clustering

Multi-view clustering is an unsupervised method which aims to enhance the clustering performance by combining the knowledge from multiple view data. Non-negative matrix factorization (NMF) is one of the most favourable multi-view clustering methods due to its strong representation ability of non-negative data. However, NMF only factorizes the data matrix into two non-negative factor matrices, which may limit its ability to learn higher level and more complex hierarchical information. To overcome this shortcoming, in this paper, we propose a multi-view clustering method based on deep graph regularized non-negative matrix factorization (MvDGNMF). MvDGNMF is able to extract more abstract representation by constructing a multilayer NMF model with graph Laplacian regularization and drive the last layer representation from each view to a common consensus representation. Meanwhile, an efficient algorithm using alternating multiplicative update rules is developed. Furthermore, in order to demonstrate the effectiveness of this proposed method, we employ several open datasets including image and text datasets to evaluate the clustering performance of MvDGNMF and the state-of-art methods.

Self-paced and auto-weighted multi-view clustering

Multi-view clustering (MVC) methods are effective approaches to enhance clustering performance by exploiting complementary information from multiple views. One main disadvantage of most existing MVC methods is that the corresponding optimization problems are non-convex and thus local optimal solutions are usually obtained. To alleviate this issue, we propose a novel multi-view clustering method equipped with self-paced learning, which first learns the MVC model with easy examples and then progressively considers complex ones from each view. In addition, a soft weighting scheme of self-paced learning is designed to further reduce the negative impact from outliers and noises. Furthermore, to consider the importance of different views, we develop an auto-weighted technique to automatically assign weights to views. The proposed model only needs the number of clusters as input and can be easily solved by an alternating optimization paradigm. Experimental results on various benchmark data sets demonstrate the effectiveness of the proposed model.

Diversity and consistency learning guided spectral embedding for multi-view clustering

Multi-view clustering aims to group data points into their classes. Exploiting the complementary information underlying multiple views to benefit the clustering performance is one of the topics of multi-view clustering. Most of existing multi-view clustering methods only constrain diversity and consistency in the data space, but not consider the diversity and consistency in the learned label space. However, It is natural to take the impacts of diversity in the learned label matrix into consideration, because different view would generate different clustering label matrix, in which some labels are consistent and some are diverse. To overcome this issue, we propose a novel multi-view clustering method (DCMSC) by constraining diversity and consistency in both the learned clustering label matrix and data space. Specifically, in the learned label space, we relax the learned common label matrix into consistent part and diverse part. Meanwhile, by applying an introduced row-aware diversity representation and l2,1-norm to constrain diverse part, wrong-labels and the influences of noises on the consistent part are reduced. In the data space, we weight each view by using a self-weight strategy. Furthermore, we conduct clustering in spectral embedded spaces instead of original data spaces, which suppresses the effect of noises and decreases redundant information. An augmented Lagrangian multiplier with alternating direction minimization (ALM-ADM) based optimization solution can guarantee the convergence of our method. Extensive experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness of our 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.

Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data

Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e., multi-view information-theoretic co-clustering (MV-ITCC). The proposed method realizes two-sided clustering for co-occurring multi-view data under the formulation of information theory. More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension. In addition, the mechanism of maximum entropy is also adopted to control the importance of different views, which can give a right balance in leveraging the agreement and disagreement. Extensive experiments are conducted on text and image multiview datasets. The results clearly demonstrate the superiority of the proposed method.

Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization

Most existing clustering approaches address multi-view clustering problems by graph regularized nonnegative matrix factorization to obtain the new representation of each view separately. And then, the additional regularization term is introduced to fuse these representations. However, the graph regularization is no guarantee that the representation obtained by mapping data points has the consistent label information with the original data. In addition, we may have a number of labeled samples in practice. This paper proposes a new semi-supervised multi-view clustering method based on Constrained Nonnegative Matrix Factorization with sparseness constraint, which is called MVCNMF. Our method first learns the representation by introducing an auxiliary matrix for each view and constructs the label constraint matrix shared among all views, where the label constraint matrix can guarantee that the label information is fused into the new representation of each view. Then, by applying the co-regularization and the sparseness constraint in auxiliary matrices, the complementary information from different views is integrated, and the robust feature of each view is extracted. Further, we also present the convergence proof and the computational complexity analysis of our method. Finally, extensive experiments on real datasets have demonstrated that our proposed approach can achieve a better clustering quality as compared to state-of-the-art multi-view clustering methods.

Parallel multi-view concept clustering in distributed computing

Multi-view clustering (MvC) is an emerging task in data mining. It aims at partitioning the data sampled from multiple views. Although a great deal of research has been done, this task remains to be very challenging. We found an important problem in performing the MvC task. MvC needs large amounts of computation. To address this problem, we propose a parallel MvC method in a distributed computing environment. The proposed method builds upon concept factorization with local manifold learning, denoted by parallel multi-view concept clustering (PMCC). Concept factorization learns a compressed representation for the data. Local manifold learning preserves the locally intrinsic geometrical structure in the data. The weight of each view is learned automatically and a cooperative normalized approach is proposed to better guide the learning of a consensus representation for all views. For the proposed PMCC architecture, the calculation of each part is independent. It is clear that our PMCC can be performed in a distributed computing environment. Experimental results using real-world datasets demonstrate the effectiveness of the proposed method.

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

ObjectivesIntegrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.MethodsEighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.ResultsTwo patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).ConclusionsThe multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.Key Points• Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology.• Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance.• Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.