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In the year 1987, Sirovich and Kirby used PCA to reduce dimensions for features vector of faces [26].
A stepped-impedance structure is integrated into this new filter simultaneously to reduce dimensions and suppress the spurious responses.
Using Deep Learning algorithms can help us to extract semantic features from a massive amount of text data in addition to reduce dimensions of the data representations.
Then, we use the feature selection method to select the feature words that can symbolize the text categories, and merge the synonym to reduce dimensions.
Recently, the authors of [71] presented linguistic hedges fuzzy classifier with selected features (LHNFCSFs) to reduce dimensions, select features, and perform classification operations.
With the additional Fourier degrees of freedom (dofs) and reduce dimensions, the accuracy of the computed natural frequencies is greatly increased.
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As stated previously, PCA is able to reduce dimension and to decorrelate the components.
Wavelet coefficients of EEG signals were assumed as feature vectors and statistical features were used to reduce dimension of those feature vectors.
Specifically, we first apply principal component analysis (PCA) to an unfolded feature vector of GEI to reduce dimension and subsequently apply LDA to get discriminant features.
In addition, to reduce dimension and avoid overfitting, CNN adopts a pooling operation to realize aggregated sampling for the previous layer network features.
Studying SNP subsets may be optimal and reduce dimension.
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