In this research article, a relationship between a scalar root finding method and matrix computations is exploited to derive new iterations to the matrix square root.
Also modification of the L-M algorithm (9)–(14) for enhanced computational speed of QNU is described as the inverse matrix computation is avoided and the Jacobian is constant for static QNU.
However, large scaled matrix computation is still treated as an open problem.
A method for constructing Poincaré projections based on the join probability matrix computation is provided.
The eigenvalue matrix computation is the most complex operation from the four stages of the PCA computation.
He assumed that the data and the signatures are stored locally on each processor's disk and the matrix computation is the part that needs to be parallelized.
In the proposed RIMC scheme, an inverse matrix computation is carried out recursively by using an RLS algorithm, and the computation circuits of {R m (k)−1, m = 0,…, N s − 1} can be communalized.
This m value is typically a constant for a certain data set; hence the input data size to the eigenvalue matrix computation is the same regardless of the size of the original data set.
Furthermore, as shown in Fig. 12, the execution time for eigenvalue matrix for DRH does not increase linearly with the size of the original data set, since the input data size to the eigenvalue matrix computation is an mXm matrix, and is the same regardless of the size of the original data set (i.e., the number of vectors).
