lenient reduction - definizione. Che cos'è lenient reduction
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Cosa (chi) è lenient reduction - definizione

PROCESS OF REDUCING THE NUMBER OF RANDOM VARIABLES UNDER CONSIDERATION
Dimension reduction; Dimensionality Reduction; Dimensionality reduction algorithm; Linear dimensionality reduction
  • A visual depiction of the resulting LDA projection for a set of 2D points.
  • A visual depiction of the resulting PCA projection for a set of 2D points.

Dimensionality reduction         
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).
Great Reduction         
LAND REFORMS IN SECOND MILLENNIUM SWEDEN; A TAKING-BACK OF POSSESSIONS FROM THE NOBILITY BY THE CROWN
Great Reduction (Sweden); Reduction (Sweden)
In the Great Reduction of 1680, by which the ancient landed nobility lost its power base, the Swedish Crown recaptured lands earlier granted to the nobility. Reductions () in Sweden and its dominions were the return to the Crown of fiefs that had been granted to the Swedish nobility.
Selective reduction         
ABORTION OF ONE OR MORE (BUT NOT ALL) FETUSES IN A MULTIPLE PREGNANCY
Fetal reduction; Pregnancy reduction, multifetal; Selective fetal reduction; Selective termination
Selective reduction is the practice of reducing the number of fetuses in a multiple pregnancy, such as quadruplets, to a twin or singleton pregnancy. The procedure is also called multifetal pregnancy reduction.

Wikipedia

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with). Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics.

Methods are commonly divided into linear and nonlinear approaches. Approaches can also be divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate step to facilitate other analyses.