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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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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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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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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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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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A survey on soft subspace clustering [PDF]
Information Sciences, 2016 This paper has been published in Information Sciences Journal in ...
Zhaohong Deng +4 more
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Subspace-based I-nice Clustering Algorithm [PDF]
Jisuanji kexueSubspace clustering of high-dimensional data is a hot issue in the field of unsupervised learning.The difficulty of subspace clustering lies in finding the appropriate subspaces and corresponding clusters.At present,the most existing subspace clustering ...
HE Yifan, HE Yulin, CUI Laizhong, HUANG Zhexue
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Low rank subspace clustering (LRSC) [PDF]
, 2014 We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors.
Favaro, Paolo, Vidal, René
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Hypergraph Convolutional Subspace Clustering With Multihop Aggregation for Hyperspectral Image
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Subspace clustering methods have become a powerful tool to cluster hyperspectral imaging (HSI) data as they ensure theoretical guarantees and empirical success.
Zijia Zhang +5 more
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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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