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A novel image hashing with low-rank sparse matrix decomposition and feature distance
Image hashing is an efficient technique of image processing for various applications, such as retrieval, copy detection and authentication. In this...
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Enhancing the Sparse Matrix Storage Using Reordering Techniques
Sparse linear algebra kernels are memory-bound routines, and their performance varies significantly according to the non-null pattern of the sparse...
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Towards Reducing Communications in Sparse Matrix Kernels
The significant presence that many-core devices like GPUs have these days, and their enormous computational power, motivates the study of sparse...
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MANet: An Architecture Adaptive Method for Sparse Matrix Format Selection
The proliferation of modern computer architectures brings a great challenge to sparse matrix-vector multiplication (SpMV), which is widely used in...
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Sparse low-rank approximation of matrix and local preservation for unsupervised image feature selection
Generalized low-rank approximation of matrix (GLRAM) is a multi-linear learning method and has been widely concerned due to its outstanding...
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swSpAMM: optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight
Although matrix multiplication plays an essential role in a wide range of applications, previous works only focus on optimizing dense or sparse...
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Performance Prediction for Sparse Matrix Vector Multiplication Using Structure-Dependent Features
Sparse matrix-vector multiplication (SpMV) is one of the most important kernels in high-performance computing, with numerous applications in...
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A sparse matrix formulation of model-based ensemble Kalman filter
We introduce a computationally efficient variant of the model-based ensemble Kalman filter (EnKF). We propose two changes to the original...
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Binary multi-view sparse subspace clustering
Multi-view subspace clustering, which partitions multi-view data into their respective underlying subspaces, has achieved the remarkable clustering...
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Semi-supervised sparse least squares support vector machine based on Mahalanobis distance
To reflect the similarity of input samples and improve the sparsity of semi-supervised least squares support vector machine (SLSSVM), a novel...
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A Resource Sharing Method of Higher Vocational Distance Online Education Based on Sparse Clustering Algorithm
There is a problem of resource redundancy in the conventional resource sharing methods of distance online education in higher vocational colleges,...
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Accelerating approximate matrix multiplication for near-sparse matrices on GPUs
Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of...
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Sparse-Aware CARM: Rooflining Locality of Sparse Computations
Sparse computation is a centre focus of modern high performance computing research. However, the complexity associated with the variety of...
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Semi-supervised sparse representation collaborative clustering of incomplete data
Sparse subspace clustering (SSC) focuses on revealing the structure and distribution of high dimensional data from an algebraic perspective. It is a...
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Riemannian thresholding methods for row-sparse and low-rank matrix recovery
In this paper, we present modifications of the iterative hard thresholding (IHT) method for recovery of jointly row-sparse and low-rank matrices. In...
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A novel mixing matrix estimation method for underdetermined blind source separation based on sparse subspace clustering
It is essential to accurately estimate the mixing matrix and determine the number of source signals in the problem of underdetermined blind source...
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Image inpainting based on sparse representation using self-similar joint sparse coding
In order to improve the sparse coding ability of over-complete dictionary and take advantage of the similarity between damaged pixels and their...
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Unsupervised RGB-T saliency detection by node classification distance and sparse constrained graph learning
Saliency detection methods which center on RGB images are sensitive to surrounding environments. Fusing complementary RGB and thermal infrared...
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Sparse spatial transformers for few-shot learning
Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled...