Effective Dimensionality Reduction in Pattern Recognition

Shobha Patil (u. a.)

ISBN 10: 3659619558 / ISBN 13: 9783659619557

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

Effective Dimensionality Reduction in Pattern Recognition | Shobha Patil (u. a.) | Taschenbuch | 160 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659619557 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 104934046

Bibliographic Details

• Title: Effective Dimensionality Reduction in Pattern Recognition
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659619558
• ISBN 13: 9783659619557
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 11D millimeters 220 millimeters width by 150 millimeters height by 11 millimeters depth
• Weight: 256 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.