A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Luong, Khanh; Nayak, Richi

doi:10.1109/icde48307.2020.00080

Cited by 20 publications

(5 citation statements)

References 31 publications

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“…Multiple experiments on simulated and real multiplex networks have been conducted to investigate the effectiveness and robustness of the proposed Mx-CRTSA compared to other community detection methods, including PM [11], RMSC [14], SCML [15], Cen-troidCoreg [16], CSNMF [20], CPNMF [20], CSNMTF [20], DiMMA [19] and 2CMV [18]. A comparison between the objective functions of the different methods considered in the experiments is illustrated in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…In [17], the proposed method relied on the probability distribution of each layer to cluster the multiplex network. In [18], an approach that relied on graph factorization to explore the consensus, as well as the complementary information coming from the different views (2CMV), was presented. In [19], the authors proposed an algorithm (DiMMA) that captured the diversity and complexity of the data from multiple perspectives in order to provide a more accurate and reliable clustering solution.…”

Section: Introductionmentioning

confidence: 99%

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Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

et al. 2023

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Complex networks arise in various fields, such as biology, sociology and communication, to model interactions among entities. Entities in many real-world systems exhibit different types of interactions, which requires modeling these type of systems properly. Multiplex networks are used to model these systems, as they can reflect the nodes’ pair-wise interactions as multiple distinct types of links across layers. Community detection is a widely studied application in network analysis as it provides insights into the structure and organization of the network. Even though multiple algorithms have been developed in the community detection field, many of them have a limited performance in the presence of noise. In this article, we develop a novel algorithm that combines tensor low-rank representation, spectral clustering and distance regularization to improve the accuracy in discovering communities in multiplex networks. The low-rank representation leads to reducing the noise and errors existing in the network and the optimization of an accurate consensus set of eigenvectors that reveals the communities in the network. Moreover, the proposed approach balances the agreement between the eigenvectors of each layer, i.e., individual subspaces, and the consensus set of eigenvectors, i.e., common subspaces, by minimizing the projection distance between them. The common and individual subspaces are computed efficiently through Tucker decomposition and modified spectral clustering, respectively. Finally, multiple experiments are conducted on real and simulated networks to evaluate the proposed approach and compare it to state-of-the-art algorithms. The proposed approach shows its robustness and efficiency in discovering the communities in multiplex networks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

et al. 2023

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“…In [18], by generating the intrinsic manifold of a multi-view data embedded from a convex hull of all the views' manifolds, the consensus manifold is learned via a linear combination of multiple manifolds of data from all views. The consensus manifold is further exploited by the novel optimal manifold concept proposed in 2CMV [30] which embeds the most consensed manifold in multi-view data. Recently, ARASP [11] proposed a novel fashion of learning the MTRD manifold, where the close and far distance information is embedded and preserved steadily for each data type.…”

Section: Nmf Clustering and Manifold Learningmentioning

confidence: 99%

“…The intra-affinity matrix on each object type is created by following the steps in [3][13] [14]. Movie (D5) and image Caltech-101 (D6) [48] datasets have been popularly used in clustering evaluation [30][32] [49]. D5 contains the set of movies represented by two different views that are moviesactors and movies-keywords.…”

Section: Datasetsmentioning

confidence: 99%

Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

Luong¹,

Nayak²

2020

IEEE Trans. Knowl. Data Eng.

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Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.

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“…In [39], a Common Subspace Fusion (CSF) model was proposed to track the objects based on a low-rank response map representation of various features and trackers. In [40], the authors proposed a Consensus and Complementary information for Multi-View data (2CMV). More precisely, the 2CMV model studied the consensus and complementary information from all views based on Coupled Matrix Factorization (CMF) and NMF.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Robust Tensor-Based Subspace Clustering

Al-sharoa

Al-wardat

2022

IEEE Access

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In this era of technology advancement, huge amount of data is collected from different disciplines. This data needs to be stored, processed and analyzed to understand its nature. Networks or graphs arise to model real-world systems in the different fields. Early work in network theory adopted simple graphs to model systems where the system's entities and interactions among them are modeled as nodes and static, single-type edges, respectively. However, this representation is considered limited when the system's entities interact through different sources. Multi-view networks have recently attracted attention due to its ability to consider the different interactions between entities explicitly. An important tool to understand the structure of multi-view networks is community detection. Community detection or clustering reveals the significant communities in the network which provides dimensionality reduction and a better understanding of the network. In this paper, a new robust clustering algorithm is proposed to detect the community structure in multi-view networks. In particular, the proposed approach constructs a 3-mode tensor from the normalized adjacency matrices that represent the different views. The constructed tensor is decomposed into a selfrepresentation and error components where the extracted self-representation tensor is used to detect the community structure of the multi-view network. Moreover, a common subspace is computed among all views where the contribution of each view to the common subspace is optimized. The proposed method is applied to several real-world data sets and the results show that the proposed method achieves the best performance compared to other state-of-the-art algorithms.INDEX TERMS Multi-view networks, optimization, low-rank representation, tensor decomposition, spectral clustering.

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A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Cited by 20 publications

References 31 publications

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

Multi-View Robust Tensor-Based Subspace Clustering

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