Results 21 to 30 of about 23,208,688 (165)
Randomized Matrix Decompositions Using R
Journal of Statistical Software, 2019 Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data ...
N. Benjamin Erichson +3 more
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Investigating the feature extraction capabilities of non-negative matrix factorisation algorithms for black-and-white images [PDF]
ITM Web of ConferencesNonnegative matrix factorisation (NMF) is a class of matrix factorisation methods to approximate a nonnegative matrix as a product of two nonnegative matrices.
Liew How Hui +2 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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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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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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Sequential and Adaptive Learning Algorithms for M-Estimation
EURASIP Journal on Advances in Signal Processing, 2008 The M-estimate of a linear observation model has many important engineering applications such as identifying a linear system under non-Gaussian noise.
Guang Deng
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, 2022 We introduce the concept of a k-dimensional matrix product D of k matrices (Formula presented.) of sizes (Formula presented.) respectively, where (Formula presented.) is equal to (Formula presented.).
Lingas, Andrzej, +2 more
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