Results 31 to 40 of about 49,736 (243)
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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Filtrated Algebraic Subspace Clustering [PDF]
SIAM Journal on Imaging Sciences, 2017 Subspace clustering is the problem of clustering data that lie close to a union of linear subspaces. In the abstract form of the problem, where no noise or other corruptions are present, the data are assumed to lie in general position inside the algebraic variety of a union of subspaces, and the objective is to decompose the variety into its ...
Tsakiris, Manolis C., Vidal, René
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Transformed Subspace Clustering [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.
Jyoti Maggu +2 more
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SSC: Statistical Subspace Clustering [PDF]
, 2005 Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It has also the benefit of providing smaller descriptions of the clusters found. Existing methods only consider
Candillier, Laurent +3 more
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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 Convex Formulation for Spectral Shrunk Clustering [PDF]
, 2014 Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the ...
Chang, Xiaojun +4 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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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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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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IET Radar, Sonar & Navigation, 2022 In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters.
Lutao Liu, Shuai Xu
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