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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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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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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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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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Hyperspectral image denoising and destri** based on sparse representation, graph Laplacian regularization and stripe low-rank property
During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For...
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Low-rank tensor learning with projection distance metric for multi-view clustering
Multi-view subspace approaches have been extensively studied for their ability to project data onto a low-dimensional space, which is in favour of...
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Multi-view low rank sparse representation method for three-way clustering
During the past years, multi-view clustering algorithms have demonstrated satisfactory clustering results by fusing the multiple views of the...
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Consensus latent incomplete multi-view clustering with low-rank tensor constraint
Traditional multi-view clustering (MVC) assumes that all views are complete and it cannot address a lack of views. In real life, a lack of views...
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Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction
Among many feature extraction technologies, non-negative matrix factorization (NMF) technology ignores the global representation of data and focuses...
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Flexible sparse robust low-rank approximation of matrix for image feature selection and classification
The left/right projection matrix and recovery matrix used for the reconstruction error in the traditional generalized low-rank approximation of...
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Hyperspectral Anomaly Detection Using Tensor Low-Rank Representation
Existing Low-rank (LR) matrix-based approaches have been widely developed for hyperspectral (HS) anomaly detection (AD). However, the 3-D intrinsic...
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Low-Rank Tensor Recovery
During data acquisition and transmission, some entries of data are missing, which will degrade the performance of subsequent data processing. Missing...
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Linear Stochastic Processes on Networks and Low Rank Graph Limits
The modelling of stochastic linear systems in large complex networks is intractable computationally and may be impossible due to data-collection...
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Sparse low-redundancy multi-label feature selection with constrained laplacian rank
As one of the crucial methods for data dimensionality reduction, multi-label feature selection aims to eliminate irrelevant and redundant features...
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Hyperchaotic bilateral random low-rank approximation random sequence generation method and its application on compressive ghost imaging
Hyperchaotic systems have been widely used in the field of communication and information security to generate random numbers due to their super-long...
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Laplacian regularized low-rank sparse representation transfer learning
In unsupervised transfer learning, it is extremely valuable to effectively extract knowledge from the vast amount of untagged data that exists by...
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An Image Denoising Algorithm Combining Global Clustering and Low-Rank Theory
In recent years, image denoising algorithms based on non-local image prior information have been extensively researched. At present, most similar...
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A multiple kinds of information extraction method for multi-view low-rank subspace clustering
Recently, multi-view subspace clustering has attracted intensive attentions due to the remarkable clustering performance by extracting abundant...
