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Learning Robust Representations for Data Analytics

2019-11-25 | |  42 |   35 |   0
Abstract Learning compact representations from highdimensional 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.

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