Results 51 to 60 of about 9,565 (204)
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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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 ...
Manolis C. Tsakiris, René Vidal
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace ...
Hongliang Lu +3 more
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An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images
Remote Sensing, 2019 Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement
Zhengyi Chen +5 more
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Low-rank sparse subspace clustering with a clean dictionary
Journal of Algorithms & Computational Technology, 2021 Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure ...
Cong-Zhe You, Zhen-Qiu Shu, Hong-Hui Fan
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Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Remote Sensing, 2021 Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
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Subspace Clustering of Subspaces: Unifying Canonical Correlation Analysis and Subspace Clustering
IEEE Transactions on Signal Processing19 pages, Submitted to IEEE Transactions on Signal ...
Paris A. Karakasis +1 more
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Subspace Clustering by Capped l(1) Norm
, 2016 Subspace clustering, as an important clustering problem, has drawn much attention in recent years. State-of-the-art methods generally try to design an efficient model to regularize the coefficient matrix while ignore the influence of the noise model on
Tao, Dacheng +8 more
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, 2008 Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.
Zimek, Arthur
core
Deep Consistent-Inherent Learning for Cross-Modal Subspace Clustering
Guidance, Navigation and ControlDeep cross-modal clustering has been developing at a rapid pace and attracted great attention. It aims to pursue a consistent subspace from different modalities by conventional neural network and achieve remarkable clustering performance.
Yuzhuo Feng, Demin Zhou
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