Results 21 to 30 of about 1,183,522 (244)
A new Approach for the Modulus-Based Matrix Splitting Algorithms
IEEE Access, 2019 We investigate the modulus-based matrix splitting iteration algorithms for solving the linear complementarity problems (LCPs) and propose a new model to solve it.
Wenpeng Wang +3 more
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Diagonal Loading Beamforming Based on Aquila Optimizer
IEEE Access, 2023 Traditional beamforming algorithms are only applicable to ideal environments. When the array antenna receives data under circumstances of small snapshots or large signal-to-noise ratio(SNR), noise eigenvalues of classic sample matrix inversion(SMI) and ...
Chao Liu, Jiaqi Zhen
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A New Parallel Matrix Multiplication Method Adapted on Fibonacci Hypercube Structure [PDF]
Journal of Sciences, Islamic Republic of Iran, 2010 The objective of this study was to develop a new optimal parallel algorithm for matrix multiplication which could run on a Fibonacci Hypercube structure. Most of the popular algorithms for parallel matrix multiplication can not run on Fibonacci Hypercube
L Jokar
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IEEE Access, 2021 Two M-decomposed based identification algorithms are proposed for large-scale systems in this study. Since the least squares algorithms involve matrix inversion calculation, they can be inefficient for large-scale systems whose information matrices are ...
Yuejiang Ji, Lixin Lv
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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]
ITM Web of Conferences, 2022 —What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
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On the geometry of border rank algorithms for matrix multiplication and other tensors with symmetry
, 2016 We establish basic information about border rank algorithms for the matrix multiplication tensor and other tensors with symmetry. We prove that border rank algorithms for tensors with symmetry (such as matrix multiplication and the determinant polynomial) come in families that include representatives with normal forms. These normal forms will be useful
Landsberg, J. M., Michałek, Mateusz
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Spectral redemption: clustering sparse networks [PDF]
, 2013 Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other ...
Krzakala, Florent +6 more
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Biased Deep Distance Factorization Algorithm for Top-N Recommendation [PDF]
Jisuanji kexue, 2021 Since traditional matrix factorization algorithms are mostly based on shallow linear models,it is difficult to learn latent factors of users and items at a deep level.When the dataset is sparse,it is inclined to overfitting.To deal with the problem,this ...
QIAN Meng-wei , GUO Yi
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A Novel Method to Implement the Matrix Pencil Super Resolution Algorithm for Indoor Positioning [PDF]
Mehran University Research Journal of Engineering and Technology, 2011 This article highlights the estimation of the results for the algorithms implemented in order to estimate the delays and distances for the indoor positioning system.
Tariq Jamil Saifullah Khanzada +2 more
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Novel Algorithms Based on Majorization Minimization for Nonnegative Matrix Factorization
IEEE Access, 2019 Matrix decomposition is ubiquitous and has applications in various fields like speech processing, data mining and image processing to name a few. Under matrix decomposition, nonnegative matrix factorization is used to decompose a nonnegative matrix into ...
R. Jyothi, Prabhu Babu, Rajendar Bahl
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