Low-Rank and Sparse Modeling for Visual Analysis: Amazon.
Sparse Linear Regression vs Sparse Signal Recovery Both solve the same optimization problem Both share efficient algorithms and theoretical results However, sparse learning setting is more challenging: We do not design the “design” matrix, but rather deal with the given data Thus, nice matrix properties may not be satisfied (and they are hard to test on a given matrix, anyway).
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.
We present a sparse fixed-rank representation approach for robust visual analysis. We impose the sparsity constraint on the learnt low-rank representation. We model the corruptions by enforcing a sparse regularizer. Its efficacy is validated by empirical studies on synthetic and real-world data.
Robust Principal Component Analysis. Low-dimensional models in high-dimensional data. Low-rank modeling for finding and harnessing low-dimensional structure of data. Robust PCA: algorithm and theory. Applications in robust batch image alignment, detection of symmetric structures in images, background subtraction, texture repairing, etc.
Low-Rank and Sparse Modeling for Visual Analysis by Yun Fu and Publisher Springer. Save up to 80% by choosing the eTextbook option for ISBN: 9783319120003, 331912000X. The print version of this textbook is ISBN: 9783319119991, 3319119990.
Low-Rank and Sparse Modeling for Visual Analysis book. Read reviews from world’s largest community for readers. This book provides a view of low-rank and.
Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision. Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high.