Multi-View Robust Tensor-Based Subspace Clustering

Abstract

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.

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

“…In [9], a unified non-negative matrix factorization algorithm that aimed to reveal the set of within-and across-layer clusters in fully-connected multi-layer networks was introduced, where the nodes were connected within and across the layers. The authors in [10] presented an approach to investigate a multi-view network structure based on a spectral clustering technique. However, they did not consider the effect of the individual layers on the common subspace.…”

Enhanced Multi-View Subspace Clustering via Twist Tensor Nuclear Norm and Constraint Propagation

“…The associate editor coordinating the review of this manuscript and approving it for publication was Qilian Liang . assign dissimilar ones to different groups. In general, multiview clustering is superior to single-view clustering since it can well exploit the complementary information in different views [2], [3].…”