Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications

By Ming Yin, Junbin Gao, Zhouchen Lin

How researchers see this paper

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

1. Vincent Wenchen Zheng
2. Tao A. Xiang
3. Yaowei Wang
4. Hongyun Cai
5. Liang Zheng
6. Kevin Chen-Chuan Chang
7. Mengyue Geng
8. Yonghong Tian

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, methodology): “NSHLRR [76] normal graph: KNN,Wij 1⁄4 1 Eq.”
• Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “To remedy the limitations, inspired by the latest development of graph regularized LRR [33], [34], we recently proposed another iterative views agreement strategy [9] with a graph regularized LRR for multiview spectral clustering, named LRRGL; to characterize the nonlinear manifold structure from ea”
• Deep Transfer Learning for Person Re-Identification (2016, methodology): “To this end, we choose a graph regularised subspace learning model [25], [26].”