Subspace Methods for Pattern Recognition in Intelligent Environment (Hardcover, 2014 ed.)

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Computational Intelligence, 552
Release date:	April 2014
First published:	2014
Editors:	Yen-Wei Chen • Lakhmi C. Jain
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Hardcover
Pages:	199
Edition:	2014 ed.
ISBN-13:	978-3-642-54850-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-54850-4
Barcode:	9783642548505

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