Results 21 to 30 of about 9,565 (204)
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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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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Fast Subspace Clustering Based on the Kronecker Product [PDF]
, 2020 Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension.
Hancock, Edwin +14 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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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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Subspace clustering for complex data.
, 2012 The increasing potential of storage technologies and information systems has opened the possibility to conveniently and affordably gather large amounts of complex data. Going beyond simple descriptions of objects by some few characteristics, such data sources range from high dimensional vector spaces over imperfect data containing errors to network ...
Günnemann, Stephan
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Latent Distribution Preserving Deep Subspace Clustering [PDF]
, 2019 Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness
Liu, X +17 more
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Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model
Axioms, 2022 Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the ...
Zhenxian Lin, Jiagang Wang, Chengmao Wu
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Deep Subspace Clustering Algorithm with Data Augmentation and Adaptive Self-Paced Learning [PDF]
Jisuanji gongcheng, 2023 Deep subspace clustering achieves better performance than traditional clustering by jointly performing self-expressed feature learning and cluster allocation.Despite the emergence of a large number of deep subspace clustering algorithms in various ...
Yuyan JIANG, Chengfeng TAO, Ping LI
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