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Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively.
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Principal Component Analysis (PCA) was performed to reduce dimensions from all the gene values to three dimensions for all seven samples with two biological replicates by using a standard-PCA algorithm.
The total variance can be summarized using a standard principal component analysis (PCA).
To simplify interpretation of loading- and score-plots we performed 'promax' oblique rotation of first two PCA-MV components using a standard 'rotatefactors' routine from MATLAB statistics toolbox.
He denied using a standard background.
using a standard sizing gradient.
Participants responded using a standard computer keyboard.
Avoid using a standard electrical box.
Suppose the largest eigenvalue of K SVM= X T X is σ and its corresponding eigenvector is v, then define the PCA correction kernel K PCA= K SVM−σ vv T. pcaSVM means that we use a standard SVM with kernel matrix K PCA.
We construct a vector from all RGB pixel values under the mask and use a standard approach, based on the singular value decomposition (SVD), to estimate the global average face, μ; the PCA components (Eigenfaces), E; and the coefficients for each image, C i. Figure 4 shows the top three Eigenfaces and corresponding distribution of PCA coefficients mapped by image location.
"Use a standard woman.
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