2021
DOI: 10.1109/access.2021.3107579
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A General Method for Generating Discrete Orthogonal Matrices

Abstract: Discrete orthogonal matrices have applications in information coding and cryptography. It is often challenging to generate discrete orthogonal matrices. A common approach widely in use is to discretize continuous orthogonal functions that have been discovered. The need of such continuous functions is restrictive. Polynomials, as the simplest class of continuous functions, are widely studied for their orthogonalality, to serve the purpose of generating orthogonal matrices. However, beginning with continuous ort… Show more

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Cited by 8 publications
(6 citation statements)
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“…In Equation ( 1 ), is a matrix [ 36 ] having the property , where takes values within a range depending on the specific situation. and are the weights developed from the 10 anchor points from the input image and the 10 transformed anchor points in the transferred image, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…In Equation ( 1 ), is a matrix [ 36 ] having the property , where takes values within a range depending on the specific situation. and are the weights developed from the 10 anchor points from the input image and the 10 transformed anchor points in the transferred image, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…In practice, the M i matrices are 2 × 2 orthogonal matrices obtained by the Gram-Schmidt orthogonalization [17][18][19]. It's important to note that we are only considering 2D angularly rotated vectors, and no translation is required in our cases.…”
Section: Multiple-decouple and Fusion Modulementioning
confidence: 99%
“…In order to apply the Naïve‐Bayes classifier to the extraction of continuous data and estimate the class probability of Pfalse(Cfalse)$P(C)$ and the conditional probability of Pfalse(Xifalse|Cfalse)$P(X_i|C)$ with i=1,2,$i=1,2,\ldots$ in Figure 4, we should first discretise the received features with the aim of converting continuous values to discrete values. A simple way to do this is to use median‐based discretization, which converts continuous features into a range of the set false{0.0,1.0false}$\{0.0,1.0\}$, and then we can use the discrete orthogonal matrix generation method, which is the most frequently applied discretisation method in the literature [15]. After, the Naïve‐Bayes can be written in the discriminative way of neural network functions.…”
Section: Naïve‐bayes Classifiermentioning
confidence: 99%
“…in Figure 4, we should first discretise the received features with the aim of converting continuous values to discrete values. A simple way to do this is to use medianbased discretization, which converts continuous features into a range of the set {0.0, 1.0}, and then we can use the discrete orthogonal matrix generation method, which is the most frequently applied discretisation method in the literature [15]. After, the Naïve-Bayes can be written in the discriminative way of neural network functions.…”
Section: Fig 4 Probabilistic Oriented Graph Of the Naïve Bayesmentioning
confidence: 99%