Subspace Methods for Pattern Recognition in Intelligent Environment (Paperback)

Yen-Wei Chen

ISBN 10: 3662501902 / ISBN 13: 9783662501900

Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2016

Riguardo questo articolo

Paperback. 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. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9783662501900

Dati bibliografici

• Titolo: Subspace Methods for Pattern Recognition in Intelligent Environment (Paperback)
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3662501902
• ISBN 13: 9783662501900
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

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.