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An Overlapping Subspace K-Means Clustering Algorithm [PDF]
Jisuanji gongcheng, 2020 Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
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Remote Sensing, 2022 Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results.
Qiliang Liu, Weihua Huan, Min Deng
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Multi-Partitions Subspace Clustering
Mathematics, 2020 In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand.
Vincent Vandewalle
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Multiple Kernel Subspace Clustering Based on Consensus Hilbert Space and Second-Order Neighbors
IEEE Access, 2020 How to deal with data sets in high-dimensional space is the focus of image processing. At present, subspace clustering method is one of the most commonly used methods for processing high-dimensional data sets. Traditional subspace clustering assumes that
Zhongyuan Wang, Jinglei Liu
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High Density Subspace Clustering Algorithm for High Dimensional Data
Journal of Harbin University of Science and Technology, 2020 Highdimensional data have the characteristics of sparsity and vulnerability to dimension disaster, which makes it is difficult to ensure the precision and efficiency of high dimensional data clustering Therefore the method of subspace clustering is ...
WAN Jing +3 more
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Diffusion Subspace Clustering for Hyperspectral Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral image (HSI) subspace clustering remains a challenging task due to the poor spatial and rich spectral resolutions of HSIs. Most of the existing HSI subspace clustering approaches just extract the spatial and spectral features, ignoring the ...
Jiaxin Chen +3 more
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Local Connectivity Enhanced Sparse Representation
IEEE Access, 2020 During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the large subspace number (LSN) subspace clustering has great ...
Kewei Tang +6 more
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Sparse Subspace Learning Based on Learnable Constraints for Image Clustering
IEEE Access, 2023 Sparse subspace clustering is a widely used method for clustering high dimensional data, but the traditional method is complex and requires prior information that can be difficult to obtain in unsupervised scenarios.
Siyuan Zhao
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IEEE Transactions on Signal Processing, 2018 The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly ...
Panagiotis A. Traganitis +1 more
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ClustNails: Visual Analysis of Subspace Clusters
Tsinghua Science and Technology, 2012 Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only
Andrada Tatu +6 more
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