Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering.

In this paper, we propose a novel graph clustering framework for robust subspace clustering. ... By incorporating a geometry-aware term with the spectral clustering objective, we encourage our framework to be robust to noise and outliers in given affinity matrices. ... Subspace Structure-aware Spectral Clustering To improve the robustness of spectral clustering in the subspace clustering problem, we consider using the data's geometric structure in the ambient space for ...

doi:10.1109/iccv.2019.00997 dblp:conf/iccv/YamaguchiIKK19 fatcat:e7tgojtrpjdd7echlci7gyhoo4

4, 5] Structure-Aware Affinity Graphs Contributions: a generalised data similarity inference framework  measure similarity via discriminative feature subspaces  well motivated by information-theoretic ... Challenging data: • High-dimensional • Heterogeneous • Noisy Typical pipeline of spectral clustering Problem Definition 1 Robust to noisy/irrelevant features: define data pairwise similarity ... Affinity graph (b) Spectral clustering (c) Cluster formation (a) Notations: ...

doi:10.1109/cvpr.2014.188 dblp:conf/cvpr/ZhuLG14 fatcat:lp6oy6qbinaohdhuy7pkyjm5ha

The proposed sparse deep subspace clustering approach efficiently identifies the underlying nonlinear subspace structure of the data and organizes the data accordingly. ... The work introduces a novel, robust sparsity measure to obtain a powerful self-representation and ameliorated performance compared to the prevalent subspace clustering methods. ... The subspace clustering framework included an improved robust sparsity term in the self-expressive layer and identified the latent nonlinear manifold structure ubiquitous in the data. ...

doi:10.1109/jstars.2022.3172112 fatcat:sdrvf2r5ind23p2iruylfmgccy

DOAJ

In this paper, we propose a new algorithm for subspace clustering of data, where the data consists of several possibly intersected manifolds. ... This has caused the development of new nonlinear techniques to cluster subspaces of high-dimensional data. ... Spectral Clustering Spectral clustering has been widely used as a clustering method for high-dimensional data. ...

doi:10.1016/j.patrec.2015.09.001 fatcat:zlqkyd4qevgkbnfni5jzyhalvu

To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific ... However, it is necessary to take the local structure of each view into consideration, because different views would present different geometric structures while admitting the same cluster structure. ... for multi-view subspace clustering. ...

arXiv:2007.12829v1 fatcat:mbagkzkh6zekdmfvda4eiii5k4

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. ... Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. ... ACKNOWLEDGMENT This paper was in part supported by Grants from the Natural Science Foundation of China (Nos. 61806045 and 61572111) and Fundamental Research Fund for the Central Universities of China ( ...

arXiv:1911.09290v1 fatcat:wh7ynj5lmzh5ffmvp4i47vzovq

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. ... Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. ... ACKNOWLEDGMENT This paper was in part supported by Grants from the Natural Science Foundation of China (Nos. 61806045 and 61572111) and Fundamental Research Fund for the Central Universities of China ( ...

doi:10.1609/aaai.v34i04.5867 fatcat:spfgkergmrcjtivcqgx2uiuxo4

In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures. ... In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced. ... To understand the process of data evolution and their specific parsimonious structures, online subspace clustering methods which are built on the spectral subspace clustering algorithms are proposed. ...

arXiv:2107.10484v1 fatcat:7fbsqplfwjcxlgbec7766jnx6y

Open Access

Our model is based on the union-of-subspaces (UoS) framework, and integrates spectral clustering into the LRR optimization problem for better subspace clustering results. ... We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for unsupervised learning of human action attributes ... CLUSTERING-AWARE STRUCTURE-CONSTRAINED LOW-RANK REPRESENTATION In this section, we propose our clustering-aware structure-constrained LRR (CS-LRR) model for learning the action attributes using the feature ...

arXiv:1612.07857v2 fatcat:hqeenseabbdk7e75xqzidsbdza

Multiple Versions

Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces. ... L2 and nuclear norm regularization often improve connectivity, but give a subspace-preserving affinity only for independent subspaces. ... The Structured Sparse Subspace Clustering (SSSC) [18] integrates the two stages, affinity learning and spectral clustering, into one unified optimization framework. ...

arXiv:1608.01793v1 fatcat:anhezfsyqncbfl3xo7lb6gjuxa

By representing the limited amount of supervisory information as a pairwise constraint matrix, we observe that the ideal affinity matrix for clustering shares the same low-rank structure as the ideal pairwise ... The code is publicly available at https://github.com/GuanxingLu/Subspace-Clustering. ... Finally, we apply spectral clustering [17] on W = (|Z| + |Z |)/2 to get the subspace segmentation. B. ...

arXiv:2205.10481v1 fatcat:t4fktw4orngoxcowxmezuysxdu

Most definitions provide only a single clustering solution For example, K -MEANS Aims at a single partitioning of the data Each object is assigned to exactly one cluster Aims at one clustering solution ... One set of K clusters forming the resulting groups of objects ⇒ In contrast, we focus on multiple clustering solutions... ... extension of DBSCAN (Ester et al., 1996) Enhanced density notion compared to grid-based techniques Arbitrary shaped clusters and noise robustness However, highly inefficient for subspace clustering INSCY ...

doi:10.1109/icdm.2010.85 dblp:conf/icdm/MullerGFS10 fatcat:uaqjt4khojfcjdji34qwgpnn4y

This paper aims to present a survey of five types of clustering methods in the perspective of optimization methodology, including center-based methods, convex clustering, spectral clustering, subspace ... Preliminary numerical experiments of various clustering algorithms for datasets of various shapes are provided to show the preference and specificity of each algorithm. ... ACKNOWLEDGMENTS The authors are grateful to the editor and the anonymous reviewer for their valuable comments and suggestions toward the improvement of this paper. ...

doi:10.23952/jano.3.2021.1.09 fatcat:u2gkeeqedzaftaxmywi5ch2nsq

First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. ... To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. ... structure, we can get the affinity matrix W = 1/2(U + U T and perform spectral clustering on such a subspace affinity matrix. ...

doi:10.3390/app12105094 fatcat:ttqo7dn4pzgptlsurmu6vqx6ni

DOAJ

In this manner, the low-dimensional subspace structures of the (implicitly) mapped data are retained and manifested in the high-dimensional feature space. ... kernels[Patel 2014] and other state-of-the-art linear subspace clustering methods. ... We have shown by extensive experiments that the proposed method significantly outperforms kernel subspace clustering with pre-defined kernels and the state-of-the-art linear subspace clustering methods ...

arXiv:1707.04974v4 fatcat:7bcm6fkl5faflaro3pku4xkqdy

Multiple Versions

Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering

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Constructing Robust Affinity Graphs for Spectral Clustering

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Sparsity Regularized Deep Subspace Clustering for Multicriterion-Based Hyperspectral Band Selection

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Nonlinear subspace clustering using curvature constrained distances

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Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering [article]

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Large-scale Multi-view Subspace Clustering in Linear Time [article]

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Large-Scale Multi-View Subspace Clustering in Linear Time

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Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications [article]

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Human Action Attribute Learning From Video Data Using Low-Rank Representations [article]

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Sparse Subspace Clustering via Diffusion Process [article]

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Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [article]

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Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data

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A survey of clustering methods via optimization methodology

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Fusing Local and Global Information for One-Step Multi-View Subspace Clustering

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Adaptive Low-Rank Kernel Subspace Clustering [article]

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