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Parameterization based on inverse depths is proposed to reduce the dimension of variables in optimization.
Principal component analysis (PCA) is used to reduce the dimension of the feature space.
Feature selection using fuzzy curves has been employed to reduce the dimension of the network.
Singular Value Decomposition is designed to reduce the dimension of the uncertainty matrix.
To reduce the dimension of the feature space, the Laplacian Eigenmaps (LE) nonlinear dimensionality reduction method is implemented.
To avoid the curse of dimensionality, we reduce the dimension before applying the Quasi-Monte Carlo method.
To reduce the dimension of the features, wavelet basis decomposition is used to produce more compact features.
At first, the input database is given to the PCA algorithm to reduce the dimension of the data.
Boundary integral techniques are a popular choice in such studies because they reduce the dimension of the problem by one.
The authors propose to use the methodology to reduce the dimension of sensor data volumes to that of a single time-varying scalar measure, which is designed to emphasize possible experimental effects.
Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector.
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