Multi-view spectral clustering via sparse graph learning

Hu, Zhanxuan; Nie, Feiping; Chang, Wei; Hao, Shuzheng; Wang, Rong; Li, Xuelong

doi:10.1016/j.neucom.2019.12.004

Cited by 94 publications

(14 citation statements)

References 9 publications

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“…The locations where the clusters are highly dense provide information-theoretic optimality. According to the minimum description length principle of (Hu et al, 2020), data compression can be performed for the considered clusters. Alternatively, one can also represent this fact, since the compression of the data is well understood by the sense of the regularities captured.…”

Section: Clustering Algorithm In Homogenous Groupsmentioning

confidence: 99%

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

Altheyabi¹

2022

AJRSP

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In network security, various protocols exist, but these cannot be said to be secure. Moreover, is not easy to train the end-users, and this process is time-consuming as well. It can be said this way, that it takes much time for an individual to become a good cybersecurity professional. Many hackers and illegal agents try to take advantage of the vulnerabilities through various incremental penetrations that can compromise the critical systems. The conventional tools available for this purpose are not enough to handle things as desired. Risks are always present, and with dynamically evolving networks, they are very likely to lead to serious incidents. This research work has proposed a model to visualize and predict cyber-attacks in complex, multilayered networks. The calculation will correspond to the cyber software vulnerabilities in the networks within the specific domain. All the available network security conditions and the possible places where an attacker can exploit the system are summarized.

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Section: Clustering Algorithm In Homogenous Groupsmentioning

confidence: 99%

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

Altheyabi¹

2022

AJRSP

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“…It can deal with input with nonnegative elements. 7) SMVSC [44] learns a consistent similarity matrix with sparse structure from multiple views. 8) DiMSC 8 [14] utilizes the HSIC (Hilbert Schmidt Independence Criterion ) as the diversity term to explore the complementarity information among multi-view representations.…”

Section: A． Experimental Settingmentioning

confidence: 99%

Multi-View Image Clustering via Representations Fusion Method With Semi-Nonnegative Matrix Factorization

et al. 2021

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Multi-view clustering aims to obtain the perfect clusters with a set of feature sets. Many methods learn a common agreement among views to achieve this. However, they may fail to use the specific unique feature of each view, which is important to describe the intrinsic characteristics of data. Unlike many works, we treat each view as a subset feature of data and fuse representations from those unique views to learn an integrated graph for clustering. We propose a novel representations fusion method for MVC. In this method, a regularized semi-nonnegative matrix factorization is proposed to learn the lowdimensional representation of each view, in which, a regularization term is designed to keep the neighboring structure in new low-dimensional space, and it is further updated and utilized to guide the learning process. Then the learned representations is fused to get an integrated graph for describing the image data. Finally, extensive experiments on several real image datasets show that our method achieves better performance than the state-of-the-art methods.

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“…Zhu et al 27 proposed a one‐step multiview clustering method to resolve the problem in a two‐step strategy. Hu et al 28 learned the consensus similarity matrix with a sparse structure from multiple views via a simple model. Kang et al 29 used the idea of anchor graphs and proposed a large‐scale spectral clustering method in linear time.…”

Section: Related Workmentioning

confidence: 99%

Kernel‐based low‐rank tensorized multiview spectral clustering

Liu

et al. 2020

Int J Intell Syst

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Multi-view spectral clustering via sparse graph learning

Cited by 94 publications

References 9 publications

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

Multi-View Image Clustering via Representations Fusion Method With Semi-Nonnegative Matrix Factorization

Kernel‐based low‐rank tensorized multiview spectral clustering

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