Low-Rank and Sparse Modeling for Visual Analysis

Softcover Nachdruck of the original 1. Auflage 2014
ISBN: 978-3-319-35567-2
Verlag: Springer

Buch, Englisch, 236 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 3752 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Fu, Yun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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.

Über Autor(innen)

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