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These approximations are obtained using interpolated proper orthogonal modes of smaller dimensional models.
All the phylogenetic trees t sharing a given topology also exhibit the same split set S, and are such that | e | t = 0 whenever e ∉ S. The BHV tree space can therefore be understood as a collection of smaller dimensional positive orthants embedded jointly in R 2 n − 1, each associated to a particular tree topology.
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The goal of many feature space transformation techniques is to project the N high-dimensional feature vectors {x 1,⋯,x N } of size n into a smaller dimensional subspace of size m using a unitary projection matrix W ∈ R n × m y k = W T x k ; with x k ∈ R n × 1, y k ∈ R m × 1, m ≤ n. (5).
For the local SURF features, we transform the resulting feature vectors separately into a smaller dimensional subspace of size 50 for every of the six used facial fiducial points before concatenating them to the final feature vector.
In many practical applications, a goal is to find a reduced basis when the input space belongs to a smaller dimensional subspace of coarse-level inputs.
Actually such popular methods are suited for data which are very high dimensional (e.g. functions) or for geometrical or spatial random objects, but not for datasets with an high number of (rather small dimensional) data.
This effect is more prominent for the ultrathin as compared to the thin NW arrays because of the small dimensional quantum confinement and higher oxygen vacancies in the former.
SMO works by breaking the large QP problem into a series of smaller 2-dimensional sub-problems that may be solved analytically, eliminating the need for numerical optimization algorithms such as conjugate gradient methods.
The use of single images or small dimensional AVHRR data sets (less then 10 layers) does not result in acceptable performances, while the use of multispectral and multitemporal databases improved the classification performance very significantly.
Merging of small two-dimensional clusters into large ones (Fig. 4g) took place.
The ability of STM to access the morphology of small three-dimensional Ag clusters (d < 2 nm) supported on graphite was investigated.
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