Results 141 to 150 of about 55,051 (186)

Data-driven identification of biological systems using multi-scale analysis. [PDF]

PLoS Comput Biol
Muhammed I, Manias DM, Goussis DA, Hatzikirou H.   +3 more
europepmc   +1 more source

Principal Component Analysis in Space Forms. [PDF]

IEEE Trans Signal Process
Tabaghi P, Khanzadeh M, Wang Y, Mirarab S.   +3 more
europepmc   +1 more source

Thermalization without Detailed Balance: Population Oscillations in the Absence of Coherences. [PDF]

J Phys Chem Lett
Blum S, Gelbwaser-Klimovsky D.
europepmc   +1 more source

<i>g</i>-factor symmetry and topology in semiconductor band states. [PDF]

Proc Natl Acad Sci U S A
Sharma M, DiVincenzo DP.
europepmc   +1 more source

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Ros T, Férat V, Huang Y, Colangelo C, Kia SM, Wolfers T, Vulliemoz S, Michela A.   +7 more
europepmc   +1 more source

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Spectral Signal Denoising Algorithm Based On Modified Singular Spectrum Analysis Analysis

2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC), 2023
A new singular spectrum analysis denoising algorithm is proposed to effectively remove photon noise and detector noise in fiber Bragg gratings. This algorithm represents a one-dimensional spectral signal as a trajectory matrix and obtains eigenvalues and
Xiangxin Shao, Ruifeng Xing, Jiacheng Li, Yongxiang Jiang, Hong Jiang   +4 more
semanticscholar   +1 more source

Eigenvectors and eigenvalues in a random subspace of a tensor product

Inventiones Mathematicae, 2012
Given two positive integers n and k and a parameter t ∈ (0, 1), we choose at random a vector subspace Vn ⊂ C k ⊗ C n of dimension N ∼ tnk. We show that the set of k-tuples of singular values of all unit vectors in Vn fills asymptotically (as n tends to ...
S. Belinschi, B. Collins, I. Nechita
semanticscholar   +3 more sources

Sharp error bounds for approximate eigenvalues and singular values from subspace methods

arXiv.org
Subspace methods are commonly used for finding approximate eigenvalues and singular values of large-scale matrices. Once a subspace is found, the Rayleigh-Ritz method (for symmetric eigenvalue problems) and Petrov-Galerkin projection (for singular values)
Irina-Beatrice Haas, Yuji Nakatsukasa
semanticscholar   +1 more source

Computation of Eigenvectors and Eigenvalues and the Singular Value Decomposition

1998
Before we discuss methods for computing eigenvalues, we mention an interesting observation. Consider the polynomial, f(& λ), $$ {{\lambda }^{p}} + {{a}_{{p - 1}}}{{\lambda }^{{p - 1}}} + ... + {{a}_{1}}\lambda + {{a}_{0}} $$ Now form the matrix, A, $$ \left[ \begin{gathered} 0 1 0 ... 0 \hfill \\ 0 0 1 ... 0 \hfill \\ \ddots \hfill \\ 0 0 0 .
openaire   +1 more source

Uniform Approximation of Eigenproblems of a Large-Scale Parameter-Dependent Hermitian Matrix

arXiv.org
We consider the uniform approximation of the smallest eigenvalue of a large parameter-dependent Hermitian matrix by that of a smaller counterpart obtained through projections.
Mattia Manucci, E. Mengi, Nicola Guglielmi   +2 more
semanticscholar   +1 more source