Fast ℓ1-minimization algorithms and an application in robust face recognition: A review

By Allen Yuqing Yang, S. Sastry, Arvind Ganesh, Yi Ma. 2010 IEEE International Conference on Image Processing, 2010

476 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1310ea938f9bdca75a5d5347b380a5c4a1d2cc0a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Jian Yang
2. Yi Wu
3. Tien-Ping Tan
4. Yong Xu
5. Xuelong Li
6. Ran He
7. Zheng Zhang
8. David Dapeng Zhang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Jiashi Feng
5. Lei Zhang
6. Xiaogang Wang
7. Fahad Shahbaz Khan
8. Yi Yang

Citations in the corpus

• Real time robust L1 tracker using accelerated proximal gradient approach (2012, background): “[25] has done a comprehensive study of the 1 norm related minimization on robust face recognition.”
• AnomalyNet: An Anomaly Detection Network for Video Surveillance (2019, background): “Although a number of methods have been proposed [17], [18], they have still suffered from following four limitations.”
• Structured AutoEncoders for Subspace Clustering (2018, methodology): “tations, we employ the Homotopy algorithm [45] to solve 1-minimization problem of SSC, KSSC, and StructAE (in the case of sparsity).”
• A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “Further detailed analyses and inference information related to proximity algorithms can be found in the literature [29], [83].”
• The German Traffic Sign Recognition Benchmark: A multi-class classification competition (2011, unknown): “l1-minimization problem formulated as in [20].”
• Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, methodology): “Recent theoretical analysis [12] and experimental results [13] show that even if corruptions are high, one can almost recover corrupted data using 1-based techniques.”
• Fast L1-Minimization Algorithms For Robust Face Recognition (2010, unknown): “An preliminary version of the work was previously published in the Proceedings of the International Conference on Image Processing in 2010 [1]. sparse (i.e., most of its entries in the canonical coordinates are zero), and the sensing matrix A is incoherent with the basis under which x 0 is sparse…”