Sentence examples for a small dimensional from inspiring English sources

In this context, an experimental technique is proposed to characterize a small dimensional structure surrounded by various air pressure levels.

The TFC membrane synthesized on a small dimensional hollow fiber support, which was spun from a P84 co-polyimide/ethylene glycol (EG /N-methyl-2-pyrrolidinone (NMP) dopEG /N-methyl-2-pyrrolidinone of a watEG /N-methyl-2-pyrrolidinone most imprEG /N-methyl-2-pyrrolidinone, 12 W m−2 at 21 bar usiNMPwater andopeM NaCl asolution.

As in multiscale finite element methods (MsFEMs), the main idea of the proposed approach is to construct a small dimensional local solution space that can be used to generate an efficient and accurate approximation to the multiscale solution with a potentially high dimensional input parameter space.

Both global and local features are first projected into a smaller dimensional subspace using LPP.

Each feature vector was transformed separately into a smaller dimensional subspace using LPP before classifying the concatenated final feature vector using a SVM with an RBF kernel.

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.

Note that for every of the six facial interest points, we transform the resulting SURF descriptors separately into a smaller dimensional subspace before concatenating them to get the final local feature vector.

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.

Although the proposed model may be computationally time-demanding and it may take time to find the optimal solution for large-sized network design, yet it can easily be converted to a smaller dimensional problem and solved.

Since this high-dimensional feature space is too large to perform fast and robust face recognition in practice, dimensionality reduction techniques like principal component analysis (PCA) [21], linear discriminant analysis (LDA) [22], or locality preserving projections (LPP) [23] can be used to project the vectorized face images into a smaller dimensional subspace.

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).