Learning Robust Representations for Data Analytics
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4010-4010.
Abstract
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.
Sheng Li. 2016. Learning Robust Representations for Data Analytics. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 4010–4010. https://ijcai.org/proceedings/2016/579
Li, S. (2016). Learning Robust Representations for Data Analytics. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 (pp. 4010–4010). IJCAI. https://ijcai.org/proceedings/2016/579
@inproceedings{ijcai2016p579,
title = {Learning Robust Representations for Data Analytics},
author = {Li, Sheng},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4010--4010},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/579},
}
TY - CPAPER TI - Learning Robust Representations for Data Analytics AU - Li, Sheng T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4010 EP - 4010 UR - https://ijcai.org/proceedings/2016/579 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
