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An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data.
An optimum allocation-based principal component analysis method was proposed by Siuly and Li [8] to extract key features for the classification of multi-class EEG signals from epileptic EEG data.
One is the algorithms without the label information, such as, principle component analysis [ 36], latent Dirichlet allocation [ 37], and sufficient dimension reduction [ 38].
These are probabilistic unsupervised models for finding latent components in documents, alternatively called Latent Dirichlet Allocation (LDA; Blei et al., 2003) or discrete Principal Component Analysis (dPCA; Buntine and Jakulin, 2004).
We based the allocation of the items of Qualcibo into domains on factor analysis (principal component analysis with varimax rotation) and face validity as judged by the investigators [ 10].
Principal component analysis (PCA) was used to search for clustering among the study participants by caries risk group allocation or alteration in caries risk level from 2005 to 2011.
Figure 5: Principal component analysis.
Principal component analysis with genome sequence data.
Principal component analysis of hg R1a subclades.
Factor analysis and independent component analysis.
(A) Mode 1 in ABCG2 principal component analysis; (B) Mode 2 in ABCG2 principal component analysis.
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