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PCA is then used to reduce the dimensions of these feature vectors.
PCA is a mathematical procedure to reduce the dimensions of data by linear fitting.
As in PCA, principal components are constructed to reduce the dimensions of X.
To reduce the dimensions of the array data we performed the principal component analysis (PCA).
To reduce the dimensions of the photo, click Image > Resize/Resample.
In [9] and [10], LS algorithms are considered to reduce the dimension of the system.
Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimension of the 27-dimension polarimetric features.
Finally, whitening is applied to reduce the dimension of acoustic feature vectors to k a elements.
Section 2.2 treats modal reduction to reduce the dimension of the generated models.
Ojala [11] proposed ULBP to reduce the dimension of the LBP.
SVD was used to reduce the dimension of signal data.
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