Exact(8)
The fundamental objective of this step is dimensionality reduction by representing face space in lower dimensional space by generating features that are optimally uncorrelated.
Obtaining these new variables in lower dimensional space is a well known problem in multivariate data analysis and is known as "Dimensionality Reduction".
The two sets of variables can be associated with a pair of linear transforms (projectors) such that the correlation between the projections of the variables in lower dimensional space through these linear transforms are mutually maximized.
This is reasonable since in lower dimensional spaces the shape and orientation of the point clouds must be correctly estimated and taken into account to identify the clusters in a proper way (see Fig. 4).
Such interaction effects were observed in earlier work in lower dimensional feature spaces [17], but were absent in both experiments described in this report.
This enables us to model high dimensional epigenomic data using methods that are more effective in lower dimensional contexts (Fig. 4).
Similar(52)
Thus the very notion of dimensionality can be said to acquire an 'emergent' nature: although the individual particles move on a three-dimensional lattice, their collective behaviour occurs in lower-dimensional space.
The resulting designs have a "checker-board" pattern in lower-dimensional projections, in contrast to grid projection that occurs with orthogonal arrays.
The concept of outliers is defined as sparsely populated patterns in lower-dimensional subspaces.
The individual core tensor in lower-dimensional space acts as the output of the multi-linear model.
There is, however, a rapidly growing literature on the possibility of time travel in lower-dimensional supersymmetric cousins of string theory.
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