Error-robust multi-view clustering

Najafi, Mehrnaz; He, Lifang; Yu, Philip S.

doi:10.1109/bigdata.2017.8257989

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Evaluating the robustness of the proposed RGCN and MVRGCN models against outlier-contaminated datasets, and conducting experiments on various architectures for robust deep autoendoers when using two separate deep autoencoders to capture clean low-rank and sparse components are considered as other directions for the future work. It is worth mentioning that the results for Fox dataset does not outperform state-of-theart methods based on Markov chains e.g., [17]. My intuition is that those results are achievable with training the models for more epochs and trying other architectures for robust deep autoencoders.…”

Section: Discussionmentioning

confidence: 90%

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An Introduction to Robust Graph Convolutional Networks

Najafi¹,

Yu²

2021

Preprint

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Graph convolutional neural networks (GCNs) generalize tradition convolutional neural networks (CNNs) from low-dimensional regular graphs (e.g., image) to high dimensional irregular graphs (e.g., text documents on word embeddings). Due to inevitable faulty data collection instruments, deceptive data manipulation, or other system errors, the data might be error-contaminated. Even a small amount of error such as noise can compromise the ability of GCNs and render them inadmissible to a large extent. The key challenge is how to effectively and efficiently employ GCNs in the presence of erroneous data. In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly. Experimental results on various real-world datasets demonstrate the superiority of the proposed model over the baseline methods and its robustness against different types of error.

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Section: Discussionmentioning

confidence: 90%

“…Improving robustness of machine learning algorithms has received considerable attention recently [14,[17][18][19]. Existing methods for improving robustness of the deep learning models can be roughly classified into four categories.…”

Section: Related Workmentioning

confidence: 99%

An Introduction to Robust Graph Convolutional Networks

Najafi¹,

Yu²

2021

Preprint

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“…Consequently, Multi-View Clustering (MVC) approaches have recently arisen to overcome the disadvantages of single-view clustering [16], [17]. These approaches include multi-view clustering based on LRR (MVC-LRR) [17]- [19], multi-view clustering based on robust principal component analysis (MVC-RPCA) [20]- [22] and multi-view clustering based on graphs (MVC-G) [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…However, ETLMSC follows a two-step strategy to construct the affinity matrix from the recovered clean tensor which is used to detect the network's structure. In [22], the authors studied the different possible errors in the multi-view features and proposed an error robust multi-view spectral clustering model. In [29], the authors proposed a multi-view subspace clustering model based on the transition probability matrix learning and a nonconvex low-rank tensor approximation instead of a convex nuclear norm.…”

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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“…To overcome this limitation, Xia et al [15] applied LRR for multi-view clustering to learn a low-rank transition probability matrix as the input of the standard Markov chain clustering method. Taking the different types of noise in samples into account, Najafi et al [16] combined the low-rank approximation with error learning to eliminate noise and outliers. The work in [17] used low-rank and sparse constraints for multi-view clustering simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

2021

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Multi-view clustering has been deeply explored since the compatible and complementary information among views can be well captured. Recently, the low-rank tensor representation-based methods have effectively improved the clustering performance by exploring high-order correlations between multiple views. However, most of them often express the low-rank structure of the self-representative tensor by the sum of unfolded matrix nuclear norms, which may cause the loss of information in the tensor structure. In addition, the amount of effective information in all views is not consistent, and it is unreasonable to treat their contribution to clustering equally. To address the above issues, we propose a novel weighted low-rank tensor representation (WLRTR) method for multi-view subspace clustering, which encodes the low-rank structure of the representation tensor through Tucker decomposition and weights the core tensor to retain the main information of the views. Under the augmented Lagrangian method framework, an iterative algorithm is designed to solve the WLRTR method. Numerical studies on four real databases have proved that WLRTR is superior to eight state-of-the-art clustering methods.

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Error-robust multi-view clustering

Cited by 13 publications

References 15 publications

An Introduction to Robust Graph Convolutional Networks

An Introduction to Robust Graph Convolutional Networks

Multi-View Robust Tensor-Based Subspace Clustering

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

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