Results 31 to 40 of about 541,400 (289)
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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Diversity-induced Multi-view Subspace Clustering Algorithm with Grouping Effect [PDF]
Jisuanji gongchengThe multi-view subspace clustering algorithm, a type of multi-view clustering algorithm, emphasizes discovering potential subspaces in multi-view data and clustering based on these subspaces.
ZHANG Yuechen, GE Hongwei, LI Ting
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Hypergraph-Supervised Deep Subspace Clustering
Mathematics, 2021 Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays
Yu Hu, Hongmin Cai
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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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The Annals of Statistics, 2014 Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009) 2790-2797] to cluster noisy data, and ...
Soltanolkotabi, Mahdi +2 more
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Subspace Clustering of High-Dimensional Data: An Evolutionary Approach
Applied Computational Intelligence and Soft Computing, 2013 Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full ...
Singh Vijendra, Sahoo Laxman
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Group-invariant Subspace Clustering [PDF]
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 Proceedings of Allerton ...
Shuchin Aeron, Eric Kernfeld
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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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Dimension Selected Subspace Clustering [PDF]
, 2021 Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set.
Chambers, Jonathon +7 more
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Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO.
Qinqin Fan +3 more
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