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Projections of the data set onto these eigenfunctions reduces the data set to a set of data coefficients.
(Void circles: projections of the data points to the axis planes).
In the work by Tatu et al. [26], 2D projections of the data in alternative subspaces were applied to identify complementary, orthogonal, or redundant subspaces; again, the approach was applicable to different subspace selection methods.
Corollary 1 states that sparse random projections of the data vector and any set of n vectors can produce estimates of their inner products to within a small error.
These LDA bi-plots (see Figure 2) comprise: (i) Projections of the data points, marked by groups, on the plane spanned by the first and second LDA directions.
Principal components analysis (PCA -based methods for analyzing PCA -based structure, like EIGENSTRAT (Price et al. 2006) and Smethods (Patterson et al. 2006), construct low-dimensional projections oforhe danalyzingmaximally retain the variance-covariance structure among the sampopulationpestructure
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The same is illustrated in Fig. 2b, that is, the projection of the data into the S T plane.
The orthogonal projection of the data onto a finite basis is typically approached by the inversion of a Gram matrix involving the inner products of the basis functions.
This article presents a new method, CovSel, which tackles these two problems by following this procedure: (1) variable selection step by step on the basis of their global covariance with all the responses; and (2) projection of the data orthogonally to the selected variable.
The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L2 sense, i.e., which minimizes the mean square error.
A geographical projection of the data reveals a non-uniform distribution of the basins of fans, and likely of voters, for the different participants.
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