Results 41 to 50 of about 44,503 (183)
Efficient Thresholded Correlation using Truncated Singular Value Decomposition
, 2015 Efficiently computing a subset of a correlation matrix consisting of values above a specified threshold is important to many practical applications. Real-world problems in genomics, machine learning, finance other applications can produce correlation matrices too large to explicitly form and tractably compute. Often, only values corresponding to highly-
Baglama, James +3 more
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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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Data for indirect load case estimation of ice-induced moments from shaft line torque measurements
Data in Brief, 2018 During ice navigation, blade measurements of ice-induced moments on ship propellers, are challenged by the harsh operating environment. To overcome this problem, shaft line measurements are performed inboard, and the required propeller loads are ...
R.J.O. de Waal, A. Bekker, P.S. Heyns
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An Out of Memory tSVD for Big-Data Factorization
IEEE Access, 2020 Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada +4 more
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Dynamic quantum clustering: a method for visual exploration of structures in data
, 2009 A given set of data-points in some feature space may be associated with a Schrodinger equation whose potential is determined by the data. This is known to lead to good clustering solutions.
B. D. Ripley +6 more
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Advances in Mechanical Engineering, 2017 The mechanical system applied in industry and manufacturing fields is generally a complex multi-stage isolation system, which contains a lot of connection parts.
Rong Guo +3 more
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Applied Mathematics in Science and EngineeringIll-posed problems are prevalent in geodetic and geophysical data processing, significantly impacting the traditional least squares (LS) estimation.
Xinna Li +4 more
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A Combined Use of TSVD and Tikhonov Regularization for Mass Flux Solution in Tibetan Plateau
Remote Sensing, 2020 Limited by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) measurement principle and sensors, the spatial resolution of mass flux solutions is about 2–3° in mid-latitudes at monthly intervals.
Tianyi Chen +3 more
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Selecting the rank of truncated SVD by Maximum Approximation Capacity
, 2011 Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$ approximation of a given input matrix. Selecting the appropriate rank $k$ defines a critical model order choice in most applications of SVD.
Buhmann, Joachim M., Frank, Mario
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Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
Sensors, 2021 For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy.
Wei Cheng +6 more
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