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Low-Rank and Sparse Modeling for Visual Analysis

 Buch
Sofort lieferbar | Lieferzeit:3-5 Tage I
ISBN-13:
9783319119991
Einband:
Buch
Erscheinungsdatum:
14.12.2014
Seiten:
236
Autor:
Yun Fu
Gewicht:
492 g
Format:
244x161x17 mm
Sprache:
Englisch
Beschreibung:

Covers the most state-of-the-art topics of sparse and low-rank modeling
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.
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.

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