Effective Dimensionality Reduction in Pattern Recognition

Language: English

Published by LAP LAMBERT Academic Publishing Dez 2014, 2014

3659619558 / 9783659619557

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This item is printed on demand - Print on Demand Titel. Neuware -Advances in data collection and storage capabilities have led to an information overload in most sciences. Such datasets present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data is the number of variables that are measured on each observation. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are 'important' for understanding the underlying phenomena of interest. It is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data.PCA is a way of identifying patterns in data, and re-expressing the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, PCA is a powerful tool for analyzing data. The other main advantage of PCA is that once you have found these patterns in the data, you can compress the data by reducing the number of dimensions, without much loss of information.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch.…

Seller Inventory # 9783659619557

Title
Effective Dimensionality Reduction in Pattern Recognition
Author
Shobha Patil
Publisher
LAP LAMBERT Academic Publishing Dez 2014
Publication year
2014
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659619558
ISBN 13
9783659619557
Item weight
256 grams
Dimensions
220x150x11 mm

buchversandmimpf2000

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