Linear and Nonlinear Subspace Methods for Pattern Classification PDF Download
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Author: Bilal Barkana
Publisher:
ISBN:
Category : Algebras, Linear
Languages : en
Pages : 196
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Book Description
Author: Bilal Barkana
Publisher:
ISBN:
Category : Algebras, Linear
Languages : en
Pages : 196
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Book Description
Author: Yen-Wei Chen
Publisher: Springer
ISBN: 3642548512
Category : Technology & Engineering
Languages : en
Pages : 210
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Book Description
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.
Author: Erkki Oja
Publisher: John Wiley & Sons
ISBN:
Category : Psychology
Languages : en
Pages : 208
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Book Description
Discusses the fundamentals of subspace methods & the different approaches taken; concentrates on the learning subspace method used for automatic speech recognition & more generally for the classification of spectra.
Author: H. J. Greenberg
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Lior Rokach
Publisher: World Scientific
ISBN: 9814271063
Category : Computers
Languages : en
Pages : 242
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Book Description
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Author: H. J. Greenberg
Publisher:
ISBN:
Category :
Languages : en
Pages : 307
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Book Description
Author: Haiping Lu
Publisher: CRC Press
ISBN: 1439857245
Category : Computers
Languages : en
Pages : 298
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Book Description
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today’s most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB® source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html
Author: Anand Rangarajan
Publisher: Springer Science & Business Media
ISBN: 3540302875
Category : Computers
Languages : en
Pages : 671
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Book Description
This book constitutes the refereed proceedings of the 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005, held in St. Augustine, FL, USA in November 2005. The 24 revised full papers and 18 poster papers presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on probabilistic and informational approaches, combinatorial approaches, variational approaches, and other approaches and applications.
Author: Sameer Singh
Publisher: Springer Science & Business Media
ISBN: 3540287574
Category : Computers
Languages : en
Pages : 713
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Book Description
The two volume set LNCS 3686 and LNCS 3687 constitutes the refereed proceedings of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, held in Bath, UK in August 2005. The papers submitted to ICAPR 2005 were thoroughly reviewed by up to three referees per paper and less than 40% of the submitted papers were accepted. The first volume includes 73 contributions related to Pattern Recognition and Data Mining (which included papers from the tracks of pattern recognition methods, knowledge and learning, and data mining); topics addressed are pattern recognition, data mining, signal processing and OCR/ document analysis. The second volume contains 87 contributions related to Pattern Recognition and Image Analysis (which included papers from the applications track) and deals with security and surveillance, biometrics, image processing and medical imaging. It also contains papers from the Workshop on Pattern Recognition for Crime Prevention.
Author: Jianxin Wu
Publisher: Cambridge University Press
ISBN: 1108483461
Category : Computers
Languages : en
Pages : 401
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Book Description
An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.
