Search · four archives
Search · four archives
13 papers · ranked by Valyu relevance
Marco Congedo, Bijan Afsari, Alexandre Barachant, Maher Moakher + 1 more
'Jesus Malo'] We explore the connection between two problems that have arisen independently in the signal processing and related fields: the estimation of the geometric mean of a set of symmetric positive definite (SPD) matrices and their approximate joint diagonalization (AJD). Today there is a considerable interest…
Xiao-Feng Gong, Ke Wang, Qiu-Hua Lin, Zhi-Wen Liu + 1 more
Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a…
Feng Miao, Rongzhen Zhao, Xianli Wang, Leilei Jia
During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring…
Haoze He, Daniel Kressner, Bor Plestenjak
It is well known that a family of ${n}\times{n}$ commuting matrices can be simultaneously triangularized by a unitary similarity transformation. The diagonal entries of the triangular matrices define the n joint eigenvalues of the family. In this work, we consider the task of numerically computing approximations to…
Joni Virta, Niko Lietzén, Pauliina Ilmonen, Klaus Nordhausen
We propose a novel method for tensorial-independent component analysis. Our approach is based on TJADE and k-JADE, two recently proposed generalizations of the classical JADE algorithm. Our novel method achieves the consistency and the limiting distribution of TJADE under mild assumptions and at the same time offers…
Haoze He, Daniel Kressner
We present and analyze a simple numerical method that diagonalizes a complex normal matrix A by diagonalizing the Hermitian matrix obtained from a random linear combination of the Hermitian and skew-Hermitian parts of A.
Riccardo Alessandro, Ivan Giannì, Federica Pes, Tommaso Nottoli + 1 more
Equations Revisited: A Simple and Efficient Iterative Algorithm Authors: ['Riccardo Alessandro' 'Ivan Giannì' 'Federica Pes' 'Tommaso Nottoli' 'Filippo Lipparini'] We present an algorithm to solve the linear response equations for Hartree-Fock, Density Functional Theory, and the Multiconfigurational Self-Consistent…
Georg Hahn, Sharon M. Lutz, Julian Hecker, Dmitry Prokopenko + 4 more
'Michael H. Cho' 'Edwin K. Silverman' 'Scott T. Weiss' 'Christoph Lange'] The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for…
Wei Pan, Jing Wang, Deyan Sun
The diagonalization of matrices may be the top priority in the application of modern physics. In this paper, we numerically demonstrate that, for real symmetric random matrices with non-positive off-diagonal elements, a universal scaling relationship between the eigenvector and matrix elements exists. Namely, each…
Benjamin Lieser, Georgy Belousov, Johannes Söding
Background Most popular tools for reconstructing phylogenetic trees from multiple sequence alignments use a model of molecular evolution in which a single substitution matrix or a small set of fixed matrices are shared between all columns. Models with column-specific rate matrices can in principle be fit by automatic…
Benjamin Krakoff, Susan M. Mniszewski, Christian F. A. Negre, Itay Hen
We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix which is based on solving a sequence of Quadratic Binary Optimization problems. This algorithm is robust across many different classes of symmetric matrices; It can compute the eigenvector/eigenvalue pair…
Nurdan Ayse Saran, Fatih Nar, Charles Elkan
This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus…
Takuya Okuyama, André Röhm, Takatomo Mihana, Makoto Naruse + 1 more
Matrix multiplication is important in various information-processing applications, including the computation of eigenvalues and eigenvectors, and in combinatorial optimization algorithms. Therefore, reducing the computation time of matrix products is essential to speed up scientific and practical calculations. Several…