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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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Subspace Clustering via Good Neighbors [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Finding the informative subspaces of high-dimensional datasets is at the core of numerous applications in computer vision, where spectral-based subspace clustering is arguably the most widely studied method due to its strong empirical performance. Such algorithms first compute an affinity matrix to construct a self-representation for each sample using ...
Jufeng Yang +4 more
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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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Inductive sparse subspace clustering [PDF]
Electronics Letters, 2013 Sparse subspace clustering (SSC) has achieved state‐of‐the‐art clustering quality by performing spectral clustering over an ℓ 1 ‐norm based similarity graph. However, SSC is a transductive method, i.e. it cannot handle out‐of‐sample data that is not used to construct the graph.
Peng, Xi, Zhang, Lei, Yi, Zhang
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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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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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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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Group-invariant Subspace Clustering [PDF]
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 Proceedings of Allerton ...
Aeron, Shuchin, Kernfeld, Eric
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Robust structure low-rank representation in latent space [PDF]
, 2019 Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for ...
Palade, Vasile +2 more
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