scholarly journals Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

2021 ◽  
Vol 13 (7) ◽  
pp. 1372
Author(s):  
Jinhuan Xu ◽  
Liang Xiao ◽  
Jingxiang Yang
Keyword(s):  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Feature Space ◽  
Low Rank ◽  
Great Success ◽  
Clustering Methods ◽  
Hypergraph Learning ◽  
Optimal Dynamic ◽  
Low Rank Representation ◽  
Original Feature

Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is unstable as the k-means method is sensitive to the initialization of the cluster centers. In order to address these issues, we propose a novel unified low-rank subspace clustering method with dynamic hypergraph for hyperspectral images (HSIs). In our method, the hypergraph is adaptively learned from the low-rank subspace feature, which can capture a more complex manifold structure effectively. In addition, we introduce a rotation matrix to simultaneously learn continuous and discrete clustering labels without any relaxing information loss. The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught property. The experimental results on real HSIs show that the proposed methods can achieve better performance compared to eight state-of-the-art clustering methods.

Robust subspace clustering based on latent low rank representation with non-negative sparse Laplacian constraints

2021 ◽  
pp. 1-15
Author(s):  
Zhixuan xu ◽  
Caikou Chen ◽  
Guojiang Han ◽  
Jun Gao
Keyword(s):  
State Of The Art ◽  
Subspace Clustering ◽  
Dimensional Subspace ◽  
Low Rank ◽  
Dimensional Structure ◽  
Clustering Methods ◽  
Benchmark Datasets ◽  
Handwritten Digit ◽  
Low Rank Representation ◽  
Low Dimensional

As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods.

Robust Structured Low-Rank Representation for Image Segmentation

2018 ◽  
Vol 27 (05) ◽  
pp. 1850020 ◽  
Author(s):  
Cong-Zhe You ◽  
Vasile Palade ◽  
Xiao-Jun Wu
Keyword(s):  
Clustering Analysis ◽  
Spectral Clustering ◽  
Optimization Problem ◽  
Subspace Clustering ◽  
Low Rank ◽  
Joint Optimization ◽  
High Dimensional ◽  
Great Success ◽  
Subspace Segmentation ◽  
Low Rank Representation

Subspace clustering analysis algorithms are often employed when dealing with high-dimensional data. As a representative approach, Low-Rank Representation (LRR) of data has achieved great success for subspace segmentation tasks in applications such as image processing. The traditional LRR-related methods consist of two separate tasks: first, the affinity graph construction by using lowrank minimization techniques, and then the spectral clustering, which is done on the affinity graph to get the final segmentation. Since these two steps are independent of each other, this method does not guarantee that the results obtained by the algorithm are globally optimal. In this paper, a method called Robust Structured Low-Rank Representation (RSLRR) is proposed, by integrating the two above mentioned tasks and solve a joint optimization problem. This paper also puts forward a method to solve the joint optimization problem, which can efficiently get both the segmentation and the structured low-rank representation. Experiments on several standard datasets show that, compared with other algorithms, the algorithm proposed in this paper can achieve better clustering results.

scholarly journals Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 3930-3937 ◽  
Author(s):  
Quanxue Gao ◽  
Wei Xia ◽  
Zhizhen Wan ◽  
Deyan Xie ◽  
Pu Zhang
Keyword(s):  
Subspace Clustering ◽  
Singular Values ◽  
Nuclear Norm ◽  
Low Rank ◽  
Clustering Methods ◽  
Norm Minimization ◽  
Illumination Changes ◽  
Low Rank Representation ◽  
Value Decomposition ◽  
Tensor Nuclear Norm

Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD, which does not make sense in the existence of noise and illumination change. To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. We further apply the WTNNM algorithm to multi-view subspace clustering by exploiting the high order correlations embedded in different views. Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance.

scholarly journals Cascaded Low Rank and Sparse Representation on Grassmann Manifolds

2018 ◽  
Author(s):  
Boyue Wang ◽  
Yongli Hu ◽  
Junbin Gao ◽  
Yanfeng Sun ◽  
Baocai Yin
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Subspace Clustering ◽  
Low Rank ◽  
Affinity Matrix ◽  
Effective Solution ◽  
Clustering Methods ◽  
Grassmann Manifolds ◽  
Trade Off ◽  
Low Rank Representation

Inspired by low rank representation and sparse subspace clustering acquiring success, ones attempt to simultaneously perform low rank and sparse constraints on the affinity matrix to improve the performance. However, it is just a trade-off between these two constraints. In this paper, we propose a novel Cascaded Low Rank and Sparse Representation (CLRSR) method for subspace clustering, which seeks the sparse expression on the former learned low rank latent representation. To make our proposed method suitable to multi-dimension or imageset data, we extend CLRSR onto Grassmann manifolds. An effective solution and its convergence analysis are also provided. The excellent experimental results demonstrate the proposed method is more robust than other state-of-the-art clustering methods on imageset data.

Hyperspectral Image Reconstruction by Latent Low-Rank Representation for Classification

2018 ◽  
Vol 15 (9) ◽  
pp. 1422-1426 ◽  
Author(s):  
Lei Pan ◽  
Heng-Chao Li ◽  
Yong-Jian Sun ◽  
Qian Du
Keyword(s):  
Image Reconstruction ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Low Rank Representation

scholarly journals Subspace clustering using a symmetric low-rank representation

2017 ◽  
Vol 127 ◽  
pp. 46-57 ◽  
Author(s):  
Jie Chen ◽  
Hua Mao ◽  
Yongsheng Sang ◽  
Zhang Yi
Keyword(s):  
Subspace Clustering ◽  
Low Rank ◽  
Low Rank Representation

Enhancing Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

2021 ◽  
Author(s):  
Wu-Chao Di ◽  
Jie Huang ◽  
Jin-Ju Wang ◽  
Ting-Zhu Huang
Keyword(s):  
Hyperspectral Image ◽  
Low Rank ◽  
Low Rank Representation

Multiview Common Subspace Clustering via Coupled Low Rank Representation

2021 ◽  
Vol 12 (4) ◽  
pp. 1-25
Author(s):  
Stanley Ebhohimhen Abhadiomhen ◽  
Zhiyang Wang ◽  
Xiangjun Shen ◽  
Jianping Fan
Keyword(s):  
Common Knowledge ◽  
Subspace Clustering ◽  
Laplacian Matrix ◽  
Low Rank ◽  
Connected Components ◽  
Similarity Matrix ◽  
Benchmark Datasets ◽  
Low Rank Representation ◽  
Low Dimensional ◽  
Shared Structure

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.

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