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Multi-view subspace clustering for learning joint representation via low-rank sparse representation
Multi-view data are generally collected from distinct sources or domains characterized by consistent and specific properties. However, most existing...
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Mixed structure low-rank representation for multi-view subspace clustering
Multi-view clustering method utilizes the diversity of multi-view information to access better clustering results than a single view. Most existing...
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CCIM-SLR: Incomplete multiview co-clustering by sparse low-rank representation
Clustering incomplete multiview data in real-world applications has become a topic of recent interest. However, producing clustering results from...
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Robust multiview spectral clustering via cooperative manifold and low rank representation induced
This paper proposes a novel multiview low-rank clustering method to learn robust multiview clustering from two different data structures, unlike...
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A fast anchor-based graph-regularized low-rank representation approach for large-scale subspace clustering
Graph-regularized low-rank representation (GLRR) is an important subspace clustering (SC) algorithm, which has been widely used in pattern...
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Data Representation and Clustering with Double Low-Rank Constraints
High-dimensional data are usually drawn from an union of multiple low-dimensional subspaces. Low-rank representation (LRR), as a multi-subspace...
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Semi-supervised Multi-view Clustering Based on Non-negative Matrix Factorization and Low-Rank Tensor Representation
Multi-view clustering methods aim to integrate the complementary information of different views to obtain accurate clustering results. However, the...
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Multi-dictionary induced low-rank representation with multi-manifold regularization
Low-rank representation (LRR) is a very competitive technique in many real-world applications for its robustness on processing noisy or corrupted...
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LatLRR-CNN: an infrared and visible image fusion method combining latent low-rank representation and CNN
While infrared images have prominent targets and stable imaging, it can hardly maintain such detailed information or quality as texture or...
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Adaptive distance penalty based nonnegative low-rank representation for semi-supervised learning
Low-rank representation (LRR) aims to find the essential structural information of the original data. It can capture global information and has...
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Nonconvex low-rank and sparse tensor representation for multi-view subspace clustering
Multi-view subspace clustering has attracted significant attention due to the popularity of multi-view datasets. The effectiveness of the existing...
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Transfer subspace learning joint low-rank representation and feature selection
Transfer learning is proposed to solve a general problem in practical applications faced by traditional machine learning methods, that is, the...
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Efficient Tensor Low-Rank Representation with a Closed Form Solution
In recent years, many tensor data processing methods have emerged. Tensor low-rank representation (TLRR) is a recently proposed tensor-based...
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Image edge preservation via low-rank residuals for robust subspace learning
In order to maintain low-rank characteristics, existing low-rank representation methods concentrate on capturing data’s low-frequency signals, which...
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Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation
Magnetic resonance imaging (MRI) has become a widely used medical imaging method. Affected by imaging mechanism, magnetic field inhomogeneity and...
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Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering
Multi-kernel subspace clustering has attracted widespread attention, because it can process nonlinear data effectively. It usually solves the...
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Reliable and robust low rank representation based noisy images multi-focus image fusion
The noisy images fusion is still a challenging multi-focus image fusion (MIF) problem as the noise is inevitable for an input image. But most of the...
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Laplacian regularized deep low-rank subspace clustering network
Self-expression-based deep subspace clustering, integrating traditional subspace clustering methods into deep learning paradigm to enhance the...
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Incomplete multi-view clustering based on weighted sparse and low rank representation
Multi-view clustering utilizes the consistency and complementarity between views to group entities well. However, in real life, the lack of instances...
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Coupled low rank representation and subspace clustering
Subspace clustering is a technique utilized to find clusters within multiple subspaces. However, most existing methods cannot obtain an accurate...
