Codd's reduction algorithm - ορισμός. Τι είναι το Codd's reduction algorithm
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Τι (ποιος) είναι Codd's reduction algorithm - ορισμός

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.

Codd's reduction algorithm      
<database> An algorithm to convert an arbitrary expression of the relational calculus to an equivalent expression of the relational algebra. This can be used as the basis of an implementation of the relational calculus. (1998-10-05)
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).
Codd's 12 rules         
RELATIONAL DATABASE DESIGN
Codd's twelve rules; Codd's rules; Codd rules; Codds laws; Codd's 12 Rules
Codd's twelve rules are a set of thirteen rules (numbered zero to twelve) proposed by Edgar F. Codd, a pioneer of the relational model for databases, designed to define what is required from a database management system in order for it to be considered relational, i.

Βικιπαίδεια

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.