Dimensionality reduction using singular vectors [PDF]
Scientific Reports, 2021 A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics.
Majid Afshar, Hamid Usefi
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Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors [PDF]
Econometrics, 2020 Large-dimensional dynamic factor models and dynamic stochastic general equilibrium models, both widely used in empirical macroeconomics, deal with singular stochastic vectors, i.e., vectors of dimension r which are driven by a q-dimensional white noise ...
Matteo Barigozzi +2 more
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Singular vectors under random perturbation [PDF]
Random Structures and Algorithms, 2011 AbstractComputing the first few singular vectors of a large matrix is a problem that frequently comes up in statistics and numerical analysis. Given the presence of noise, an exact calculation is hard to achieve, and the following problem is of importance:How much does a small perturbation to the matrix change the singular vectors?Answering this ...
Van H. Vu
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Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2009 This paper takes an in-depth look at a technique for computing filtered matrix-vector (mat-vec) products which are required in many data analysis applications. In these applications, the data matrix is multiplied by a vector and we wish to perform this product accurately in the space spanned by a few of the major singular vectors of the matrix.
Yousef Saad
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Variational Quantum Singular Value Decomposition [PDF]
Quantum, 2021 Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix.
Xin Wang, Zhixin Song, Youle Wang
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Nature of protein family signatures: insights from singular value analysis of position-specific scoring matrices. [PDF]
PLoS ONE, 2008 Position-specific scoring matrices (PSSMs) are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families.
Akira R Kinjo, Haruki Nakamura
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Covariant Lyapunov Vectors and Finite-Time Normal Modes for Geophysical Fluid Dynamical Systems
Entropy, 2023 Dynamical vectors characterizing instability and applicable as ensemble perturbations for prediction with geophysical fluid dynamical models are analysed.
Jorgen S. Frederiksen
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CMA Global Ensemble Prediction Using Singular Vectors from Background Field
应用气象学报, 2022 China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) adopts singular vector method to generate initial perturbations.
Huo Zhenhua +3 more
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Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors
IEEE Access, 2020 Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost.
Seunghyun Lee, Byung Cheol Song
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Singular Vectors From Singular Values
CoRR, 2020 8 ...
Weiwei Xu, Michael K. Ng 0001
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