Results 31 to 40 of about 280,565 (313)
Robust regularized singular value decomposition with application to mortality data [PDF]
, 2013 We develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year.
Huang, Jianhua Z. +2 more
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Accelerated random noise suppression of seismic data using compressed singular-value decomposition
矿业科学学报Random noise is one of the common background noises in seismic data, and its attenuation will directly affect the signal-to-noise ratio of seismic data, which is of great significance to improve the quality of seismic data.
SUN Chao +4 more
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Split-and-Combine Singular Value Decomposition for Large-Scale Matrix
Journal of Applied Mathematics, 2013 The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis,
Jengnan Tzeng
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The Singular Value Decomposition over Completed Idempotent Semifields
Mathematics, 2020 In this paper, we provide a basic technique for Lattice Computing: an analogue of the Singular Value Decomposition for rectangular matrices over complete idempotent semifields (i-SVD).
Francisco J. Valverde-Albacete +1 more
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Identification of Seismic Reflections Using Singular Value Decomposition [PDF]
Modeling, Identification and Control, 1986 Singular value decomposition (SVD) is applied to the identification of seismic reflections by using two different models: the impulse response model where a seismic trace is assumed to consist of a known signal pulse convolved with a reflection ...
Bjørn Ursin, Yuying Zheng
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Statistical inference based on robust low-rank data matrix approximation
, 2014 The singular value decomposition is widely used to approximate data matrices with lower rank matrices. Feng and He [Ann. Appl. Stat. 3 (2009) 1634-1654] developed tests on dimensionality of the mean structure of a data matrix based on the singular value ...
Feng, Xingdong, He, Xuming
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Very Large-Scale Singular Value Decomposition Using Tensor Train Networks [PDF]
, 2014 We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and
Cichocki, Andrzej, Lee, Namgil
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IEEE AccessImage enhancement and reconstruction is an important field of research in digital image analysis. To increase the quality of low-contrast images, a variety of image-enhancing technologies are available.
Shahzada Fahad +7 more
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Feature extraction of vibration signal of roadheader based on singular value decompositio
Gong-kuang zidonghua, 2019 In view of difficulty of dynamic load identification of roadheader, feature extraction method of vibration signal of roadheader based on singular value decomposition was proposed.
ZHANG Linfeng +5 more
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Products, coproducts and singular value decomposition
, 2004 Products and coproducts may be recognized as morphisms in a monoidal tensor category of vector spaces. To gain invariant data of these morphisms, we can use singular value decomposition which attaches singular values, ie generalized eigenvalues, to these
B. Fauser +13 more
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