Results 231 to 240 of about 150,932 (264)
Some of the next articles are maybe not open access.
Matrix decomposition and data reduction
Computers & Graphics, 1995Abstract In this paper, we present a class of decomposition techniques for data represented as matrices. The main idea is to transform a matrix into a sequence of components in order to represent and analyze the matrix in a multi-resolution setting. Data reduction is obtained by maintaining only entries of each matrix component that give significant ...
Morten Dæhlen, Per Gunnar Holm
openaire +1 more source
A decomposition of the manipulator inertia matrix
IEEE Transactions on Robotics and Automation, 1997A decomposition of the manipulator inertia matrix is essential, for example, in forward dynamics, where the joint accelerations are solved from the dynamical equations of motion. To do this, unlike a numerical algorithm, an analytical approach is suggested in this paper.
openaire +1 more source
Discriminative base decomposition for time-frequency matrix decomposition
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Time-frequency matrix (TFM) decomposition using non-negative matrix factorization (NMF) has been recently considered as a successful tool for time-frequency (TF) quantification. In this paper, we modify the constraints of traditional cost function of NMF to make the method a better fit for TF quantification, and denote the new method with NMF ...
Behnaz Ghoraani, Sridhar Krishnan 0001
openaire +1 more source
Smoothness and Periodicity of Some Matrix Decompositions
SIAM Journal on Matrix Analysis and Applications, 2001The paper deals with small orthonormal factorizations of smooth matrix-valued functions of constant rank. The authors obtain interesting results dealing with smoothness of constant rank functions and particularly for singular value decompositions and related factorizations.
Jann-Long Chern, Luca Dieci
openaire +2 more sources
The decompositional approach to matrix computation
Computing in Science & Engineering, 2000The introduction of matrix decomposition into numerical linear algebra revolutionized matrix computations. The article outlines the decompositional approach, comments on its history, and surveys the six most widely used decompositions: Cholesky decomposition; pivoted LU decomposition; QR decomposition; spectral decomposition; Schur decomposition; and ...
openaire +1 more source
On fault tolerant matrix decomposition
Journal of VLSI signal processing systems for signal, image and video technology, 1994We present a fault tolerant algorithm for matrix factorization in the presence of multiple hardware faults which can be used for solving the linear systemAx=b without determining the correctZU decomposition ofA. HereZ is eitherL for ordinary Gaussian decomposition with partial pivoting,X for pairwise or neighbor pivoting (motivated by the Gentleman ...
openaire +1 more source
Rank Decisions in Matrix Quotient Decompositions
SIAM Journal on Matrix Analysis and Applications, 2016Summary: This paper describes an orthogonal, fully rank revealing generalized (quotient) URV decomposition for a pair of rectangular matrices. The algorithm for computing the decomposition is fully rank revealing in the sense that it makes rank decisions in an order that is guaranteed to reliably determine if the pair of matrices is close to a pair ...
openaire +2 more sources