2013 FastMinimizationAlgorithmsforRo
- (Yang et al., 2013) ⇒ Allen Y. Yang, Zihan Zhou, Arvind Ganesh Balasubramanian, S. Shankar Sastry, and Yi Ma. (2013). “Fast-minimization Algorithms for Robust Face Recognition.” In: Image Processing, IEEE Transactions on, 22(8).
Subject Headings: ℓ1 Minimization.
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Abstract
l1-minimization refers to finding the minimum l1-norm solution to an underdetermined linear system </math>b=Ax</math>. Under certain conditions as described in compressive sensing theory, the minimum l1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as augmented Lagrangian methods. We conduct extensive experiments to validate and compare its performance against several popular l1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing, and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2013 FastMinimizationAlgorithmsforRo | Zihan Zhou Arvind Ganesh Balasubramanian Yi Ma Allen Y. Yang S. Shankar Sastry | Fast-minimization Algorithms for Robust Face Recognition |