Subspace Methods for Pattern Recognition in Intelligent Environment

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Subspace Methods for Pattern Recognition in Intelligent Environment is written by Yen-Wei Chen and published by Springer. It's available with International Standard Book Number or ISBN identification 3642548512 (ISBN 10) and 9783642548512 (ISBN 13).

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.