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The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric factor model is assumed.
Through data generated through the Positive Soundscapes Project, a Principal Component Analysis was conducted to decompose descriptors of the urban soundscape into two independent emotional dimensions.
The uniqueness of this data representation is in that the points are projected onto 3D space using conventional Principal Component Analysis; however the projected vertices are connected by edges only if they are defined as neighbours in the original high dimensional space.
The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space.
Families clustered with the European samples in the HapMap project based on the principal component analysis performed using the SNPRelate package [ 41] were extracted.
Principal component analysis (PCA) projects the high-dimensional data to lower dimensional space by capturing maximum variance [88].
The principal component analysis (PCA) projects by construction the initial space spanned by the different samples into a new derived space whose axes (principal components) are each other orthogonal.
To visually investigate the discriminative ability of the features, we used principal component analysis (PCA) to project the 106 dimensional space to a 3D space.
Finally, a two-dimensional principal component analysis is used to project the transformed image for feature extraction.
A PCA (principal component analysis) was done to project the data onto such a maximally varying subspace.
Principle component analysis was performed using the R-project (http://www.r-project.org/) to obtain a global overview of gene expression data.
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