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Robust Graph Dimensionality Reduction
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers. To do this, we propose simultaneously 1) adaptively learning three variables, \ie a reverse graph embedding of original data, a transformation matrix, and a graph matrix preserving the local similarity of original data in their low-dimensional intrinsic space; and
doi:10.24963/ijcai.2018/452
dblp:conf/ijcai/ZhuLYLGZ18
fatcat:aw2kxefldzdk7kmiplnngch4tm