Consensus Affinity Graph Learning for Multiple Kernel Clustering

Ren, Zhenwen; Yang, Simon X.; Sun, Quansen; Wang, Tao

doi:10.1109/tcyb.2020.3000947

Cited by 63 publications

(32 citation statements)

References 39 publications

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“…Also it is worth to mention that many new methods in multiple kernel graph-based clustering (MKGC) and multiple kernel learning (MKL) for graphbased spectral clustering has emerged in recent years. For example, Ren et al [37] proposed a new MKGC method to learn a consensus affinity graph directly and Ren et al [38] proposed a novel MKL method, namely structure-preserving multiple kernel clustering (SPMKC).…”

Section: Related Workmentioning

confidence: 99%

Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

et al. 2021

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In this paper, we focus on the multi-view subspace clustering problem under the framework of third order tensor. Recently, tensor nuclear norm (TNN) has been widely used in the multi-view subspace clustering problem. It is known that TNN is a convex surrogate of the tensor rank. However, TNN is linearly proportional to the sum of the singular values. Thus solving the TNN minimization problem will overpenalize the large singular values and lead to a sub-optimal solution. To address this issue, in this paper, a novel tighter tensor log-determinant (TLD) regularizer is proposed to better approximate the tensor rank. Although the proposed method is non-convex, a closed-form solution has been deduced via solving the Euler-Lagrange equation. A corresponding algorithm associated with augmented Lagrangian multipliers is established and the constructed convergent sequence to the Karush-Kuhn-Tucker (KKT) critical point solution is mathematically validated in detail. Extensive experiments indicate that the proposed model achieves significant improvements compared to the state-of-the-art convex subspace clustering models.

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Section: Related Workmentioning

confidence: 99%

Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

et al. 2021

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“…(3) Kernel graph fusion: Kernel graph fusion scheme aims to learn a consensus affinity graph by using graph learning paradigms, and then employs spectral clustering to segment clusters [12]. For example, structure preserving multiple kernel clustering (SPMKC) [23] integrates the global and local structure preserving by simultaneously leveraging both SESL and ANGL with a kernel affine weight strategy into a unified MKL optimization model.…”

Section: B Multiple Kernel Graph-based Clustering (Mkgc)mentioning

confidence: 99%

“…The main reasons are that (1) each kernel function has a different representation capability; (2) most suitable kernels and the associated parameters for a specific dataset are difficult to select. In this paper, to tackle the challenging problem of non-linear data clustering, we seamlessly integrate graph-based clustering (GBC) [12] and multiple kernel learning (MKL) [8], [13] into a unified objective function. This learning paradigm is dubbed as multiple kernel graph-based clustering (MKGC).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, GBC methods have been widely investigated due to their effectiveness in capturing the complex graph structure hidden in data. Usually, GBC consists of two continuous steps [12], [14]: (1) constructing an affinity graph based on graphical representations of the relationships among data points, and (2) applying spectral clustering algorithms (e.g., normalized cut and ratio cut) to obtain clustering assignments. Constructing a high-quality affinity graph plays a crucial role in GBC [15].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, MKL aims to learn a consensus kernel from multiple base kernels, which cannot only effectively handle non-linear data but also alleviate the curse of kernel choice [13]. Overall, there are three strategies for the comprehensive utilization of the given multiple base kernels, i.e., (1) using linearly kernel combination to generate a consensus kernel [18], [19]; (2) using non-linearly kernel combination to generate a consensus kernel [13], [20]- [22]; (3) changing the goal of consensus kernel learning to consensus graph learning [12], [23]. In practice, MKL has been widely used to handle non-linear data in various applications [8], [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Ren

Mukherjee

Bennis

et al. 2021

IEEE J. Select. Areas Commun.

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Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the highorder correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for nonlinear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the highorder structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.

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Adaptive partial graph learning and fusion for incomplete multi‐view clustering

et al. 2021

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Most of existing multi-view clustering methods assume that different feature views of data are fully observed. However, it is common that only portions of data features can be obtained in many practical applications.The presence of incomplete feature views hinders the performance of the conventional multi-view clustering methods to a large extent. Recently proposed incomplete multi-view clustering methods often focus on directly learning a common representation or a consensus affinity similarity graph from available feature views while ignore the valuable information hidden in the missing views. In this study, we present a novel incomplete multi-view clustering method via adaptive partial graph learning and fusion (APGLF), which can capture the local data structure of both within-view and cross-view. Specifically, we use the available data of each view to learn a corresponding view-specific partial graph, in which the within-view local structure can be well preserved. Then we design a cross-view graph fusion term to learn a consensus complete graph for different views, which can take advantage of the complementary information hidden in the viewspecific partial graphs learned from incomplete views.In addition, a rank constraint is imposed on the graph Laplacian matrix of the fused graph to better recover the optimal cluster structure of original data.

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Consensus Affinity Graph Learning for Multiple Kernel Clustering

Cited by 63 publications

References 39 publications

Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Adaptive partial graph learning and fusion for incomplete multi‐view clustering

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