Application areas
We tackle a wide variety of data-driven applications by investigating optimization and machine-learning techniques. Here are some exemplified applications, but we do not restrict ourselves to them.
Recommendation
The low-rank approximation has attracted more and more attention in the community of recommendation. A fundamental assumption in constructing matrix approximations is that the partially observed data matrix is of low rank. A matrix completion (imputation) gives a tool to predict missing entries for a recommendation system.
Color transfer
Color transfer is a method of transferring color information from a source image to a target image by considering a reference image. This problem is presented in terms of the optimal transportation problem.
Video surveillance (anomaly detection)
Video sequence understanding by online subspace learning with matrix and tensor representations.
Image processing (recovery, basis representation, and denoising)
Hyperspectral image recovery on the Ribeira dataset
Network traffic analysis (anomaly traffic detection via inverse problem)
Anomaly traffic volume detection by the inverse problem.
Wireless system architecture design
Architecture design of hybrid precoder large-scale millimeter wave (mmWave) MIMO systems.