Learning With ℓ1-Graph for Image Analysis

By Bin Cheng, Jianchao Yang, Shuicheng Yan, Yun Fu, Thomas S. Huang. IEEE Transactions on Image Processing, 2010

296 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/25f936634c9e907ac7d7acb37fef0a08ddfbf222

How researchers see this paper

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

1. Shuicheng Yan
2. Dacheng Tao
3. Lei Zhang
4. Julien Mairal
5. Yi Yang
6. Ran He
7. Jingdong Wang
8. Luc Van Gool

Citations in the corpus

• Sparse Representation for Computer Vision and Pattern Recognition (2010, background): “Thus, the ‘(1)-graph possesses all the three characteristics of a desired graph for data clustering, subspace learning, and semisupervised learning [22], [88].”
• Multi-task low-rank affinity pursuit for image segmentation (2011, background): “Accordingly, the case for the multiple types of features targeting on Problem 2.2 will be further examined with a wellestablished solution.”
• View-invariant human action recognition via robust locally adaptive multi-view learning (2015, methodology): “(7), we use the r power of α i instead of α i (Cheng et al., 2010).”
• High-resolution satellite scene classification using a sparse coding based multiple feature combination (2012, background): “…sparse coding, from image restorations (Aharon et al. 2006, Yang et al. 2008), compressive sensing (Candès et al. 2006, Candès and Tao 2006, Donoho 2006), machine learning (Yu et al. 2009, Cheng et al. 2010) to various computer vision tasks (Wright et al. 2009, Yang et al. 2009, Dai and Yang 2011).”
• Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (unknown): “Recently, the graph-based learning methods have been widely used to develop the high performance algorithms for semi-supervised learning task [13] [38] [40] [54].”
• Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, background): “Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explores their relation is still an open problem.”