Multiple kernel clustering with corrupted kernels

Li, Teng; Dou, Yong; Liu, Xinwang; Zhao, Yang; Lv, Qi

doi:10.1016/j.neucom.2017.06.044

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…(36) consists of four constraint inequalities, which is written compactly just to save space.). We now design an algorithm to this problem inspired by [33], [34], it achieves low-complexity computation via a two-stage two-step alternate manner. The proposed algorithm is outlined in Algorithm 1, where obj t denotes value of 1 1+ 2−εβ 0 εβ 1 at the t-th iteration.…”

Section: B Optimal Values Of the Rating Update Rulementioning

confidence: 99%

Game-Theoretic Design of Optimal Two-Sided Rating Protocols for Service Exchange Dilemma in Crowdsourcing

Xin

Zhang

et al. 2018

IEEE Trans.Inform.Forensic Secur.

Self Cite

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Despite the increasing popularity and successful examples of crowdsourcing, it is stripped of aureole when collective efforts are derailed or severely hindered by elaborate sabotage. A service exchange dilemma arises when non-cooperation among selfinterested users, and zero social welfare is obtained at myopic equilibrium. Traditional rating protocols are not effective to overcome the inefficiency of the socially undesirable equilibrium due to specific features of crowdsourcing: a large number of anonymous users having asymmetric service requirements, different service capabilities, and dynamically joining/leaving a crowdsourcing platform with imperfect monitoring. In this paper, we develop the first game-theoretic design of the two-sided rating protocol to stimulate cooperation among self-interested users, which consists of a recommended strategy and a rating update rule. The recommended strategy recommends a desirable behavior from three predefined plans according to intrinsic parameters, while the rating update rule involves the update of ratings of both users, and uses differential punishments that punish users with different ratings differently. By quantifying necessary and sufficient conditions for a sustainable social norm, we formulate the problem of designing an optimal two-sided rating protocol that maximizes the social welfare among all sustainable protocols, provide design guidelines for optimal two-sided rating protocols and a low-complexity algorithm to select optimal design parameters in an alternate manner. Finally, evaluation results show the validity and effectiveness of our protocol designed for service exchange dilemma in crowdsourcing.

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Section: B Optimal Values Of the Rating Update Rulementioning

confidence: 99%

Game-Theoretic Design of Optimal Two-Sided Rating Protocols for Service Exchange Dilemma in Crowdsourcing

Xin

Zhang

et al. 2018

IEEE Trans.Inform.Forensic Secur.

Self Cite

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show abstract

“…This will be a stumbling block for the practical use of kernel method in real applications. This issue is partially alleviated by multiple kernel learning (MKL) technique which lets an algorithm do the picking or combination from a set of candidate kernels [29,30]. Since the kernels might be corrupted due to the contamination of the original data with noise and outliers.…”

Section: Introductionmentioning

confidence: 99%

Low-rank kernel learning for graph-based clustering

Kang

Wen

Chen

et al. 2019

Knowledge-Based Systems

158

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Constructing the adjacency graph is fundamental to graph-based clustering.Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the choosed kernel matrix. To address this issue, previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels.By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method.

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“…Multi-view clustering (MVC) optimally integrates features from different views to improve clustering performance (Bickel and Scheffer 2004). It has been intensively studied and widely applied into various applications during the last few decade (Yu et al 2012;Li, Jiang, and Zhou 2014;Du et al 2015;Liu et al 2016;Li et al 2016;Liu et al 2017b;Li et al 2015;Cai, Nie, and Huang 2013;Liu et al 2013;Zhang et al 2015;. All these MVC algorithms assume that the views of samples are observable.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Effective Incomplete Multi-View Clustering

Liu

Zhu

et al. 2019

AAAI

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Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, overcomplicated optimization and limitedly improved clustering performance. In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. We carefully develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.

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Multiple kernel clustering with corrupted kernels

Cited by 20 publications

References 8 publications

Game-Theoretic Design of Optimal Two-Sided Rating Protocols for Service Exchange Dilemma in Crowdsourcing

Game-Theoretic Design of Optimal Two-Sided Rating Protocols for Service Exchange Dilemma in Crowdsourcing

Low-rank kernel learning for graph-based clustering

Efficient and Effective Incomplete Multi-View Clustering

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