Results 11 to 20 of about 3,534,092 (344)
Moore-Penrose inverse of a hollow symmetric matrix and a predistance matrix
Special Matrices, 2016 By a hollow symmetric matrix we mean a symmetric matrix with zero diagonal elements. The notion contains those of predistance matrix and Euclidean distance matrix as its special cases.
Kurata Hiroshi, Bapat Ravindra B.
doaj +2 more sources
Generalized Symmetric Neutrosophic Fuzzy Matrices [PDF]
Neutrosophic Sets and Systems, 2023 We develop the concept of range symmetric Neutrosophic Fuzzy Matrix and Kernel symmetric Neutrosophic Fuzzy Matrix analogous to that of an EP –matrix in the complex field. First we present equivalent characterizations of a range symmetric matrix and then
M. Anandhkumar +3 more
doaj +1 more source
Symmetric nonnegative matrix trifactorization
Linear Algebra and its Applications, 2023 The Symmetric Nonnegative Matrix Trifactorization (SN-Trifactorization) is a factorization of an $n \times n$ nonnegative symmetric matrix $A$ of the form $BCB^T$, where $C$ is a $k \times k$ symmetric matrix, and both $B$ and $C$ are required to be nonnegative.
Damjana Kokol Bukovšek, Helena Šmigoc
openaire +2 more sources
Unfolding a symmetric matrix [PDF]
Journal of Classification, 1996 Graphical displays which show inter--sample distances are important for the interpretation and presentation of multivariate data. Except when the displays are two--dimensional, however, they are often difficult to visualize as a whole. A device, based on multidimensional unfolding, is described for presenting some intrinsically high-
John C. Gower +2 more
openaire +4 more sources
Algorithms for solving a class of real quasi-symmetric Toeplitz linear systems and its applications
Electronic Research Archive, 2023 In this paper, fast numerical methods for solving the real quasi-symmetric Toeplitz linear system are studied in two stages. First, based on an order-reduction algorithm and the factorization of Toeplitz matrix inversion, a sequence of linear systems ...
Xing Zhang +3 more
doaj +1 more source
Generic symmetric matrix pencils with bounded rank [PDF]
Journal of Spectral Theory, 2018 We show that the set of $n \times n$ complex symmetric matrix pencils of rank at most $r$ is the union of the closures of $\lfloor r/2\rfloor +1$ sets of matrix pencils with some, explicitly described, complete eigenstructures.
Fernando De Ter'an +2 more
semanticscholar +1 more source
Self-Dual Codes, Symmetric Matrices, and Eigenvectors
IEEE Access, 2021 We introduce a consistent and efficient method to construct self-dual codes over $GF(q)$ using symmetric matrices and eigenvectors from a self-dual code over $GF(q)$ of smaller length where $q \equiv 1 \pmod 4$ . Using this method, which is called a
Jon-Lark Kim, Whan-Hyuk Choi
doaj +1 more source
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
On symmetrizers in quantum matrix algebras
Russian Mathematical Surveys, 2023 In this note we are dealing with a particular class of quadratic algebras -- the so-called quantum matrix algebras. The well-known examples are the algebras of quantized functions on classical Lie groups (the RTT algebras). We consider the problem of constructing some projectors on homogenous components of such algebras, which are analogs of the usual ...
Gurevich, Dmitry +2 more
openaire +2 more sources
A Hebbian/Anti-Hebbian network for online sparse dictionary learning derived from symmetric matrix factorization [PDF]
Asilomar Conference on Signals, Systems and Computers, 2014 Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor ...
Tao Hu, Cengiz Pehlevan, D. Chklovskii
semanticscholar +1 more source