Sentence examples for implementation of the pca from inspiring English sources

Implementation of the PCA took place in the R statistical package [33], and required some basic preparation of the datasets.

For example, if two of three sensors are available, then the two-sensor implementation of the PCA is used.

However, for the reasons discussed in our implementation of the PCA model, we have decided to use only the first principal component variable.

In order to facilitate a better understanding of the implementation of MDA, PCA, ANN, and MGD statistical methods and to evaluate their relative effectiveness in pattern classification, we use the experimental data sets from mouse hippocampus and the two simulated data sets from monkey cortex; all data sets contain 250 neurons and small number of repetitions of sampled stimuli.

In this article we have considered only the implementation of PCA and SPCA using the first derived variable, that is, the first principal component of all five pollutants for PCA and the first principal component of the retained pollutants for SPCA.

In comparing the two solutions of same illustrative problem presented in Tables 10 and 15 (route selection through PCA and Taguchi's method), it is observed that the implementation of PCA and Taguchi's method only in route selection decreases the total inter-cell moves whilst a slight increase in total inter-cell movement cost is also there.

However, the step-by-step implementation criteria in the analysis of the PCA resulted in three factors with 9 items that explained 77.7% of the variance.

On the other hand, MLP and RBFNN schemes presented highly limited performances over the entire track of experiments regarding the implementation of PCA.

Further in this section, proposed algorithm is modified for the implementation of PCA and Taguchi's method, whilst conclusions are drawn in "Conclusions".

We used our own implementation of the following face recognition methods: (i) PCA Mahalanobis distance (baseline) [38],   (ii) PCA Mahalanobis Cosine distance [38],   (iii) Adaboost with Local Binary Patterns (LBP) [39],   (iv) PCA LDA likelihood ratio [40].  .

The PCA was performed using the implementation of the R package FactoMineR version 1.26 (Lê et al. 2008); the permutational MANOVA was carried out using the implementation provided by the R package vegan version 2.0-10 2.0-10en et al. 2013).