- singular vector decomposition
- abbr. SVD特异向量分解
Atmospheric Sciences (English-Chinese) dictionary. 2014.
Atmospheric Sciences (English-Chinese) dictionary. 2014.
Singular value decomposition — Visualization of the SVD of a 2 dimensional, real shearing matrix M. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the action of M, which distorts the disk to an ellipse. The SVD decomposes M into… … Wikipedia
Vector field reconstruction — [ [http://prola.aps.org/pdf/PRE/v51/i5/p4262 1 Global Vector Field Reconstruction from a Chaotic Experimental Signal in Copper Electrodissolution.] Letellier C, Le Sceller L , Maréchal E, Dutertre P, Maheu B, Gouesbet G, Fei Z, Hudson JL.… … Wikipedia
Singular Spectrum Analysis — The Singular Spectrum Analysis (SSA) techniqueis a powerful technique of time series analysisincorporating the elements of classical time series analysis,multivariate statistics, multivariate geometry, dynamical systemsand signal processing. The… … Wikipedia
Matrix decomposition — In the mathematical discipline of linear algebra, a matrix decomposition is a factorization of a matrix into some canonical form. There are many different matrix decompositions; each finds use among a particular class of problems. Contents 1… … Wikipedia
Schmidt decomposition — In linear algebra, the Schmidt decomposition refers to a particular way of expressing a vector in the tensor product of two inner product spaces. It has applications in quantum information theory and plasticity. Theorem Let H 1 and H 2 be Hilbert … Wikipedia
Schur decomposition — In the mathematical discipline of linear algebra, the Schur decomposition or Schur triangulation (named after Issai Schur) is an important matrix decomposition. Statement The Schur decomposition reads as follows: if A is a n times; n square… … Wikipedia
QR decomposition — In linear algebra, the QR decomposition (also called the QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and a right triangular matrix. The QR decomposition is often used to solve the linear least squares problem … Wikipedia
LU decomposition — In linear algebra, LU decomposition (also called LU factorization) is a matrix decomposition which writes a matrix as the product of a lower triangular matrix and an upper triangular matrix. The product sometimes includes a permutation matrix as… … Wikipedia
Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… … Wikipedia
Non-linear least squares — is the form of least squares analysis which is used to fit a set of m observations with a model that is non linear in n unknown parameters (m > n). It is used in some forms of non linear regression. The basis of the method is to… … Wikipedia
Moore–Penrose pseudoinverse — In mathematics, and in particular linear algebra, a pseudoinverse A+ of a matrix A is a generalization of the inverse matrix.[1] The most widely known type of matrix pseudoinverse is the Moore–Penrose pseudoinverse, which was independently… … Wikipedia