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They are initialized as null matrices, with all of their entries being zero.

0 n×m and 1 n×m represent the n×m matrices with all elements equal to 0 and 1, respectively.

For given n, (r< n), the (ntimes n) matrix (L^) with rank-r was generated by (R_{1}R_{2}^{T}), where (R_{1}) and (R_{2}) are both random matrices with all components distributed in ([0,1]) uniformly.

An important tool in the study of majorization is the next theorem, due to Hardy et al. [3], which gives connections with matrix theory, more specifically with doubly stochastic matrices, i.e. nonnegative square matrices with all rows and columns sums being equal to one.

At each ALLR and OLAP score cutoff value, we compare each of the 540 matrices with all of the others to determine the score distributions.

It is clear to see that the kernel matrices under the underfitting bias are flat matrices with all '1' entries, but the kernel matrices under the linear kernel appear to be normal for all three data sets, even if there are explicit and implicit label skewness biases for the BreastIBC and Kidney data respectively.

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I is the identity matrix and 1 is a matrix with all ones.

Let (A=[a_{ij}]) be an (ntimes n) matrix with all diagonal entries being nonzero throughout.

where denotes the matrix with all zero entries except the entry which is equal to.

The symbol is used for the matrix with all entries equal to one.

Table 2 provides the correlation matrix with all 8 variables described above.

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