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Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors.
After the trajectory descriptors are computed, the principle component analysis (PCA) is individually applied to reduce the dimensionality of each descriptor (i.e., HOG, HOF, MBH, and STMS) by a factor of two as suggested in [24] so as to better mitigating the impact of noise.
Factor analysis, which includes principal component analysis (PCA) is a very powerful technique applied to reduce the dimensionality of a data set consisting of a large number of interrelated variables while remaining as much as possible the variability present in data set.
A multidimensional scaling[17] was applied to reduce the dimensionality of the k k−1)/2 space.
Multidimensional scaling was applied to reduce the dimensionality of the data and permit visualization.
For blind signal separation, PCA is applied to reduce the dimensionality of broadband reflectance data, to estimate the number of components used to inform a diagnosis, and to analyze if their diagnostic relevance.
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Principal component analysis (PCA) is further applied to reduce the dimensionality to 13 features.
To overcome this problem, dimension reduction methods are applied to reduce the dimensionality from p to q with q ≪ p. Dimension reduction usually consists of two types of methods, feature selection and feature extraction [ 4].
PCA is applied to reduce the dimensionality from 384 to 64.
To overcome this problem, feature selection methods are applied to reduce the dimensionality from n to k with k << n.
Following the steps of the Kaldi Librispeech s5 recipe, LDA is applied to reduce the feature dimensionality, which is then followed by MLLT to match the GMM diagonal assumption.
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