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
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
Nearest neighbor search — (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces. The problem is: given a set S of points in a metric space M and a query point… … Wikipedia
Information-based complexity — (IBC) studies optimal algorithms and computational complexity for the continuous problems which arise in physical science, economics, engineering, and mathematical finance. IBC has studied such continuous problems as path integration, partial… … Wikipedia
Quasi-Monte Carlo methods in finance — High dimensional integrals in hundreds or thousands of variables occur commonly in finance. These integrals have to be computed numerically to within a threshold epsilon. If the integral is of dimension d then in the worst case, where one has a… … Wikipedia
Richard E. Bellman — Infobox Systems scientist H region = Control Theory era = 20th century color = #B0C4DE image caption = name = Richard E. Bellman birth = birth date|1920|8|26|df=y New York City, New York death = death date and age|1984|3|19|1920|8|26|df=y school… … 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
Метод главных компонент — (англ. Principal component analysis, PCA) один из основных способов уменьшить размерность данных, потеряв наименьшее количество информации. Изобретен К. Пирсоном (англ. Karl Pearson) в 1901 г. Применяется во многих областях,… … Википедия
Clustering high-dimensional data — is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of… … 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
