The phrase "achieve dimension" is not commonly used in written English and may be unclear without context.
It could be used in contexts related to design, art, or personal growth, where one might refer to adding depth or significance to something.
Example: "In order to truly achieve dimension in your artwork, consider incorporating various textures and colors."
Alternatives: "add depth" or "gain perspective".
The eigenvectors with the larger eigenvalues are selected to reconstruct a new data set to achieve dimension reduction according to the PCA.
Principal Components Analysis (PCA) is a classical statistical tool to achieve dimension reduction through consideration of linear combinations of the original variables.
As a classical statistical tool to achieve dimension reduction, principal components (PCs) are linear combinations of the underlying variables and usually several top PCs can explain a large amount of variation in the whole dataset.
In addition, they achieve dimension reduction by summarizing the data into a small number of components or variates, which are linear combinations of the original variables.
LAPEA, in analogy to e.g. principal components analysis (PCA), is a statistical tool one can use to achieve dimension reduction of highly complex sets of (genetic) data.
The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost.
"If you withdraw all the forces without any international understanding and without any even partial solution of some of the problems, civil war in Iraq will take on even more violent forms and achieve dimensions that are probably exceeding those that brought us into Yugoslavia with military force," he said.
It achieves dimension reduction by a Petrov Galerkin projection associated with residual minimization; it delivers computational efficiency by a hyper-reduction procedure based on the 'gappy POD' technique.
Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning.
These measures were strongly inter-correlated (r>0.55), thus we achieved dimension reduction by a principal component analysis that produced a single axis to describe the male's interest towards the control and the experimental breeding situation, separately (explained variance in the three measured behaviours: control situation, 77% experimental situation, 79%).
The SIR technique is firstly adopted to achieve a dimension reduction by finding a new input vector which reduces the dimension of the original input vector without losing the essential information of model responses.
