Results 1 to 10 of about 6,789 (166)
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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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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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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Orderly Subspace Clustering [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure.
Jing Wang 0023 +5 more
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Attributed Subspace Clustering [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders.
Jing Wang 0023 +5 more
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