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Clustering quality metrics for subspace clustering
Pattern Recognition, 2020 Abstract We study the problem of clustering validation, i.e., clustering evaluation without knowledge of ground-truth labels, for the increasingly-popular framework known as subspace clustering. Existing clustering quality metrics (CQMs) rely heavily on a notion of distance between points, but common metrics fail to capture the geometry of subspace ...
John Lipor, Laura Balzano
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Transformed Subspace Clustering [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.
Jyoti Maggu +2 more
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Filtrated Algebraic Subspace Clustering [PDF]
SIAM Journal on Imaging Sciences, 2017 Subspace clustering is the problem of clustering data that lie close to a union of linear subspaces. In the abstract form of the problem, where no noise or other corruptions are present, the data are assumed to lie in general position inside the algebraic variety of a union of subspaces, and the objective is to decompose the variety into its ...
Manolis Tsakiris, René Vidal
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Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering [PDF]
Jisuanji gongcheng, 2021 The potential subspace structure of high-dimensional data can be obtained by using subspace clustering,but the existing methods can not reveal the characteristics of global low-rank structure and local sparse structure of data at the same time,which ...
TAO Yang, BAO Linglang, HU Hao
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Multi-Layer Network Community Detection Based on Sparse Subspace Clustering [PDF]
Jisuanji gongcheng, 2021 The existing subspace clustering methods are only applicable to single-layer networks, or just average the clustering results of each layer in the multi-layer network.They fail to consider the different amounts of information contained in each layer ...
SUN Dengdi, LING Yuan, DING Zhuanlian, LUO Bin
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Learnable Subspace Clustering [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 This paper studies the large-scale subspace clustering (LSSC) problem with million data points. Many popular subspace clustering methods cannot directly handle the LSSC problem although they have been considered as state-of-the-art methods for small-scale data points. A basic reason is that these methods often choose all data points as a big dictionary
Jun Li 0027 +4 more
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Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
Applied Sciences, 2022 Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects.
Yiqiang Duan +3 more
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PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark
IEEE Access, 2021 Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc.
Xiao Wen, Hu Juan
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A Fault-tolerance Subspace Clustering Algorithm in Data Mining [PDF]
Jisuanji gongcheng, 2016 In order to improve the computation efficiency of the subspace clustering algorithm,this paper gives a general fault-tolerance subspace clustering definition according to the number of objects,dimensions,mode tolerance and relative threshold constraint ...
TIAN Jinhua,SUN Li
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Multilinear subspace clustering [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union of subspaces (UOS) model.
Eric Kernfeld +3 more
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