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Figure 3 Vector model and first slide of a rolling matrix, the data illustrate the line and column numbers.
:: If the multiplication SJ is considered as a whole matrix, the data model (12) can be modeled as PARAFAC.
After the 14-dimensional feature matrix is reduced to 2-dimensional feature matrix, the data distribution of five types of gestures is shown in Fig. 7.
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank.
As an adjacency matrix, the data structure that stores an undirected graph, a similarity adjacency matrix is the data structure that stores a similarity graph and as long as a similarity adjacency matrix is filled out, the relevant similarity graph then can be plotted.
Prior to the computation of the correlation matrix, the data were normalized to take on values between 0.1 and 0.9 using the following equation: X_{n} = 0.8 times left[ {frac{{(X_{i} - X_{hbox{min} } )}}{{X_{hbox{max} } - X_{hbox{min} } }}} right] + 0.1 (5)where X n is the normalized value, X i is the measured value, X max and X min are maximum and minimum values, respectively.
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Let be the covariance matrix of the data matrix.
In this subsection, we propose an ESPRIT-based algorithm to estimate the angles and the coupling matrix from the data matrix ZTS.
A spreadsheet is used to generate a matrix and the data are 'charted' into the matrix.
The covariance matrix of the data can be defined by the -dimensional matrix (2).
The matrix S is recognized as the sample covariance matrix of the data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com