Low-Rank and Sparse Modeling for Visual Analysis

Yun Fu (Herausgeber)

Buch | Softcover

VII, 236 Seiten

2016 | 1. Softcover reprint of the original 1st ed. 2014
Springer International Publishing (Verlag)
978-3-319-35567-2 (ISBN)

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This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Yun Fu is an Assistant Professor, ECE and CS, Northeastern University

Nonlinearly Structured Low-Rank Approximation.- Latent Low-Rank Representation.- Scalable Low-Rank Representation.- Low-Rank and Sparse Dictionary Learning.- Low-Rank Transfer Learning.- Sparse Manifold Subspace Learning.- Low Rank Tensor Manifold Learning.- Low-Rank and Sparse Multi-Task Learning.- Low-Rank Outlier Detection.- Low-Rank Online Metric Learning.

Erscheinungsdatum	30.08.2016
Zusatzinfo	VII, 236 p. 66 illus., 51 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	391 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	compressive sensing • Computer Imaging, Vision, Pattern Recognition and • Computer Science • computer vision • dimensionality reduction • Image Processing • image processing and computer vision • Imaging systems and technology • Low-Rank Approximation • Low-Rank Recover • Low-Rank Representation • machine learning • pattern recognition • Signal, Image and Speech Processing • Signal Processing • Sparse Representation • subspace learning
ISBN-10	3-319-35567-8 / 3319355678
ISBN-13	978-3-319-35567-2 / 9783319355672
Zustand	Neuware