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A best matrix approximation technique for updating the analytical model is developed using the known modal parameters.
We showed here that even if the association between the original fitness measurements or their interaction scores is relatively low, their comparability can be improved by means of our matrix approximation technique.
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Fig. 1 Example of two syllables: a syllable A; b syllable B. The corresponding MT spectrograms with window length 6.9 ms, frequency resolution 1770 Hz and K=8;:c syllable A; d syllable B. The filtered ambiguity spectra: e syllable A; f syllable B. The singular value decomposition is a low-rank matrix approximation and a popular noise-reduction technique for a data matrix.
These methods enable the considerable reduction of computation complexity by using the Maclaurin's expansion approximation technique in the calculation of inverse matrix under the assumption that the CIR is changing linearly within one symbol period.
The strengths of the proposed approximation rely on the generic spatial discretization of the model equations using a continuous finite element type approximation technique, while avoiding the solution of a large linear system with a sparse mass matrix which would come along with any standard ODE solver in a classical finite element approach to advance the solution in time.
The A-harmonic approximation technique is a natural extension of harmonic approximation technique.
The coefficients related to the approximation technique are given.
We then proceed to study the expected error of matrix approximation.
The smooth approximation technique is an effective method which makes objective function solvable by smooth approximation of L1 norm[27, 27].
A similar approximation technique by trigonometric functions is commonly called Fourier expansion, and is much applied in engineering, see below.
Recent work on fast matrix approximation may help speed up these calculations and save memory capacities [34], [35].
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