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Fisher Discriminative Embedding Low-Rank Sparse Representation for Music Genre Classification
This work focuses on a music genre classification method based on a sparse low-rank representation. Sparse low-rank representation is an effective...
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Tensor low-rank representation combined with consistency and diversity exploration
In recent years, many tensor data processing methods have been proposed. Tensor low-rank representation (TLRR) is a recently proposed tensor-based...
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Low-rank Representation for Seismic Reflectivity and its Applications in Least-squares Imaging
Sparse representation and inversion have been widely used in the acquisition and processing of geophysical data. In particular, the low-rank...
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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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Non-local tensor sparse representation and tensor low rank regularization for dynamic MRI reconstruction
Dynamic Magnetic Resonance Imaging (DMRI) reconstruction is a challenging theme in image processing. A variety of dimensionality reduction methods...
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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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Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization
Incomplete multi-view clustering has attracted attention due to its ability to deal with clustering problems with incomplete information. However,...
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Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint
In this paper, to extract the manifold information from multi-view data and enhance the clustering performance of a multi-view learning method, 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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Extraction method of typical IEQ spatial distributions based on low-rank sparse representation and multi-step clustering
Indoor environment quality (IEQ) is one of the most concerned building performances during the operation stage. The non-uniform spatial distribution...
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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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Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis
In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell...
