Targeting Kollo Skewness with Random Orthogonal Matrix Simulation

2021 ◽  
Author(s):  
Carol Alexander ◽  
Xiaochun Meng ◽  
Wei Wei
Keyword(s):  
Orthogonal Matrix

scholarly journals Relative asymptotics for orthogonal matrix polynomials

2012 ◽  
Vol 437 (7) ◽  
pp. 1458-1481 ◽  
Author(s):  
A. Branquinho ◽  
F. Marcellán ◽  
A. Mendes
Keyword(s):  
Orthogonal Matrix ◽  
Matrix Polynomials ◽  
Orthogonal Matrix Polynomials

scholarly journals Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins

2016 ◽  
Vol 32 (10) ◽  
pp. 1527-1535 ◽  
Author(s):  
Martin Stražar ◽  
Marinka Žitnik ◽  
Blaž Zupan ◽  
Jernej Ule ◽  
Tomaž Curk
Keyword(s):  
Matrix Factorization ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Orthogonal Matrix ◽  
Integrative Analysis

scholarly journals ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER

2016 ◽  
Vol III-7 ◽  
pp. 103-110
Author(s):  
P. Srestasathiern ◽  
S. Lawawirojwong ◽  
R. Suwantong ◽  
P Phuthong
Keyword(s):  
Probability Distribution ◽  
Orthogonal Matrix ◽  
Rotation Matrix ◽  
Sampling Scheme ◽  
Uniform Rotation ◽  
Householder Transformation ◽  
Orthogonal Matrices ◽  
Matrix Sampling ◽  
Paper Address ◽  
Multidimensional Probability

This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.

scholarly journals Construction of Measurement Matrix Based on Cyclic Direct Product and QR Decomposition for Sensing and Reconstruction of Underwater Echo

2018 ◽  
Vol 8 (12) ◽  
pp. 2510 ◽  
Author(s):  
Tongjing Sun ◽  
Hong Cao ◽  
Philippe Blondel ◽  
Yunfei Guo ◽  
Han Shentu
Keyword(s):  
Direct Product ◽  
Hadamard Matrix ◽  
Triangular Matrix ◽  
Field Measurements ◽  
Qr Decomposition ◽  
Orthogonal Matrix ◽  
Measurement Matrix ◽  
Reconstruction Accuracy ◽  
Weak Signals ◽  
Construction Time

Compressive sensing is a very attractive technique to detect weak signals in a noisy background, and to overcome limitations from traditional Nyquist sampling. A very important part of this approach is the measurement matrix and how it relates to hardware implementation. However, reconstruction accuracy, resistance to noise and construction time are still open challenges. To address these problems, we propose a measurement matrix based on a cyclic direct product and QR decomposition (the product of an orthogonal matrix Q and an upper triangular matrix R). Using the definition and properties of a direct product, a set of high-dimensional orthogonal column vectors is first established by a finite number of cyclic direct product operations on low-dimension orthogonal “seed” vectors, followed by QR decomposition to yield the orthogonal matrix, whose corresponding rows are selected to form the measurement matrix. We demonstrate this approach with simulations and field measurements of a scaled submarine in a freshwater lake, at frequencies of 40 kHz–80 kHz. The results clearly show the advantage of this method in terms of reconstruction accuracy, signal-to-noise ratio (SNR) enhancement, and construction time, by comparison with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix. In particular, for weak signals with an SNR less than 0 dB, this method still achieves an SNR increase using less data.

Sign in / Sign up

Export Citation Format

Share Document