Sentence examples for pca I from inspiring English sources

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables.

For patients with classic nonmetastatic, locally advanced PCa (i.e., clinical stage T3), the risk of local progression is real and standard therapies have not resulted in significant long-term overall survival (OS .41 The development of improved radiation treatment for patients with adverse prognostic factors has posed a considerable challenge.

The 13 factors extracted by PCA (i.e. 100% of information) were included in DA.

In this paper, we focus on an orthogonal linear transform of face images, namely PCA (i.e., Eigenfaces).

To explore all the potential differences in the metabolic profiles between the BD and HC groups, the 1H-NMR spectra (Fig. 1a) were subjected to PCA, i.e., the spectral data obtained for 76 blood serum samples that were acquired as 228 spectra were used in PCA.

Theoretically, the first principal component, PC 1, collects the information that is common to all bands used as input data to the PCA, i.e., the spatial information, while the spectral information that is specific to each band is captured in the other principal components [42, 33].

The intracranial atherosclerotic lesions were localized mainly at PCA, I-ICA, ACA, MCA and the cervical atherosclerotic lesions were localized mainly at CCA. *Based on the rating of more affected side in case of bilateral vessel lesions.

Several reported loadings did not approach the minimal level recommended for PCAs (i.e.,.32, Tabachnick and Fidell 2007), and there was insufficient information provided about the cross-loadings.

A total of 108 staff will participate in the study, including 54 senior staff (i.e., 3 facility managers and/or RNs from each of the 18 facilities) and 54 senior PCAs (i.e., 3 PCAs from each of the 18 facilities).

In addition, lack of unidimensionality may exist when the eigenvalue of the first contrast in a Rasch principal components analysis of the residuals (PCA-R) (i.e., the first component after the Rasch component has been removed) is larger than 2.0, and when the variance explained by the Rasch component is small (e.g., < 40%%) [ 38].

At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition.