Learning with Robust Data Representations: Methodologies and Applications
Extracting knowledge from high-dimensional and large-scale data plays an important role in many real-world applications. Following a bottom-up framework, we can represent low-level raw features as mid-level codings, or even high-level representations. In this talk, I will introduce some approaches we have designed recently, including mid-level feature learning (e.g., low-rank codings, dictionary learning) and high-level feature learning (e.g., graph construction, subspace learning). Real-world applications such as image classification, person re-identification, outlier detection and recommender system will be discussed as well.
About the Presenter:
Sheng Li is a fourth year Ph.D. candidate in the SMILE Lab, advised by Prof. Yun Fu. He received the B.Eng. degree in computer science and engineering and the M.Eng. degree in information security from Nanjing University of Posts and Telecommunications, China, in 2010 and 2012, respectively. His research interests include low-rank matrix recovery, multi-view learning, dictionary learning, and semi-supervised learning.