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.Low-Rank Transfer Learning Ming Shao, Dmitry Kit and Yun Fu Abstract Real- world visual data are expensive to label for ... Transfer learning provides techniques for transferring learned knowledge from asourcedomain to atarget domain byanbsp;...
|Title||:||Low-Rank and Sparse Modeling for Visual Analysis|
|Publisher||:||Springer - 2014-10-30|