Learning robust representations for data analytics
Sheng Li · International Joint Conference on Artificial Intelligence · 2016
Learning compact representations from high-dimensional and large-scale data plays an essential role in many real-world applications. However, many existing methods show limited performance when data are contaminated with severe noise. To address this challenge, we have proposed several effective methods to extract robust data representations, such as balanced graphs, discriminative subspaces, and robust dictionaries. In addition, several topics are provided as future work.