- dimensionality reduction
- понижение размерности
The New English-Russian Dictionary of Radio-electronics. F.V Lisovsky . 2005.
dimensionality reduction
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Nonlinear dimensionality reduction — High dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies on an embedded non linear manifold within… … Wikipedia
Multifactor dimensionality reduction — (MDR) is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable. MDR was designed specifically to identify interactions among discrete… … Wikipedia
Dimension reduction — For dimensional reduction in physics, see Dimensional reduction. In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature… … Wikipedia
Dimensional reduction — This article is on dimensional reduction in physics. For the statistics concept, see Dimensionality reduction. In physics, a theory in D spacetime dimensions can be redefined in a lower number of dimensions d, by taking all the fields to be… … Wikipedia
Curse of dimensionality — The curse of dimensionality refers to various phenomena that arise when analyzing and organizing high dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low dimensional settings such as the physical space… … Wikipedia
Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… … Wikipedia
Feature extraction — In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not… … Wikipedia
Multilinear subspace learning — (MSL) aims to learn a specific small part of a large space of multidimensional objects having a particular desired property. It is a dimensionality reduction approach for finding a low dimensional representation with certain preferred… … Wikipedia
Independent component analysis — (ICA) is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non Gaussian source signals. It is a special case of blind source separation. Definition When… … Wikipedia
Granular computing — is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information.… … Wikipedia
Semantic mapping (statistics) — The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a data… … Wikipedia
