Low-Rank Approximation
Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic.
More relevant information
- C. Yang, L. Wan, S. Zhang and H. Zhao. Accounting for Non-Genetic Factors by Low-Rank Representation and Sparse Regression for eQTL Mapping. Bioinformatics. 2013. [The yeast data set used in this paper]
- X. Zhou, C. Yang and W. Yu. Moving objects segmentation by detecting contiguous outlier in low-rank representation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 35(3): 597-610, 2013. [software]
- X, Zhou, C. Yang, H. Zhao and W. Yu. Low-Rank Modeling and Its Applications in Image Analysis. ACM Computing Surveys. Vol. 47, No. 2, Article 36, January 2015. [The Matlab code to produce the results presented in this paper]