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This vector with high dimensionality was reduced to the lower dimensional subspace by PCA plus NFLE.
Briefly, LSA is a dimensionality reduction technique that projects terms and documents (abstracts) into a lower dimensional space.
PLS reduces X to a lower dimensional subspace (k ≪ p).
Theorem 1.1 and Theorem 1.2 describe the lower dimensional problems.
These lower dimensional feature vectors are then used for classification.
GSPT uses these lower dimensional subsystems, studied in Sect.
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These techniques are a straightforward extension of lower-dimensional displays, but are often hard to interpret with increasing dimensionality.
Step 2: project (D_{j,ik}) on lower-dimensional space.
In dimensional reduction, the symmetry cells are lower-dimensional submanifolds.
The latter case results in a lower-dimensional map.
Distributed systems consist of interconnected, lower-dimensional subsystems.
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