Results 231 to 240 of about 28,414 (283)
Some of the next articles are maybe not open access.
Perturbation Analysis for t-Product-Based Tensor Inverse, Moore-Penrose Inverse and Tensor System
Communication on Applied Mathematics and Computation, 2021This paper establishes some perturbation analysis for the tensor inverse, the tensor Moore-Penrose inverse, and the tensor system based on the t-product.
Zhengbang Cao, Pengpeng Xie
semanticscholar +1 more source
Multimodel Feature Reinforcement Framework Using Moore–Penrose Inverse for Big Data Analysis
IEEE Transactions on Neural Networks and Learning Systems, 2020Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition
Wandong Zhang +3 more
semanticscholar +1 more source
On the algebraic structure of the Moore-Penrose inverse of a polynomial matrix
IMA Journal of Mathematical Control and Information, 2021This work establishes the connection between the finite and infinite algebraic structure of singular polynomial matrices and their Moore–Penrose (MP) inverse. The uniqueness of the MP inverse leads to the assumption that such a relation must exist. It is
Ioannis S. Kafetzis, N. Karampetakis
semanticscholar +1 more source
Enclosing Moore–Penrose inverses
Calcolo, 2020An algorithm is proposed for computing intervals containing the Moore–Penrose inverses. For developing this algorithm, we analyze the Ben-Israel iteration. We particularly emphasize that the algorithm is applicable even for rank deficient matrices.
openaire +2 more sources
On matrices whose Moore–Penrose inverse is idempotent
Linear and multilinear algebra, 2020The paper investigates the class of square matrices which have idempotent Moore–Penrose inverse. A number of original characteristics of the class are derived and new properties identified.
O. Baksalary, G. Trenkler
semanticscholar +1 more source
Perturbation Analysis of the Moore-Penrose Inverse and the Weighted Moore-Penrose Inverse
2018Let A be a given matrix. When computing a generalized inverse of A, due to rounding error, we actually obtain the generalized inverse of a perturbed matrix \(B=A+E\) of A. It is natural to ask if the generalized inverse of B is close to that of A when the perturbation E is sufficiently small.
Sanzheng Qiao, Guorong Wang, Yimin Wei
openaire +2 more sources
1997
By definition, a generalized inverse of an m × n matrix A is any n × m matrix G such that AGA = A. Except for the special case where A is a (square) nonsingular matrix, A has an infinite number of generalized inverses (as discussed in Section 9.2a).
openaire +2 more sources
By definition, a generalized inverse of an m × n matrix A is any n × m matrix G such that AGA = A. Except for the special case where A is a (square) nonsingular matrix, A has an infinite number of generalized inverses (as discussed in Section 9.2a).
openaire +2 more sources
Computing Moore–Penrose Inverses with Polynomials in Matrices
The American Mathematical Monthly, 2021This article proposes a method for computing the Moore–Penrose inverse of a complex matrix using polynomials in matrices. Such a method is valid for all matrices and does not involve spectral calculation, which could be infeasible when the size of the matrix is large.
openaire +3 more sources
Spectral Analysis of Large Reflexive Generalized Inverse and Moore-Penrose Inverse Matrices
Recent Developments in Multivariate and Random Matrix Analysis, 2020A reflexive generalized inverse and the Moore-Penrose inverse are often confused in statistical literature but in fact they have completely different behaviour in case the population covariance matrix is not a multiple of identity.
Taras Bodnar, Nestor Parolya
semanticscholar +1 more source
Moore-Penrose Inverses and Singular Values [PDF]
, 1999 Let H be a Hilbert space and let A be a bounded linear operator on H. Then sp A*A ⊂[0,∞), and the non-negative square roots of the numbers in sp A*A are called the singular values of A. The set of the singular values of A will be denoted byΣ(A), $$ \sum {\left( A \right)} : = \left\{ {s \in \left[ {\left.
Albrecht Böttcher, Bernd Silbermann
openaire +1 more source