Nonnegative Sparse and KNN graph for semi-supervised learning
- https://doi.org/10.2991/ameii-16.2016.223How to use a DOI?
- Sparse graph, KNN graph, NSKNN-graph, semi-supervised learning
For the graph-based semi-supervised learning, the performance of a classifier is very sensitive to the structure of the graph. So constructing a good graph to represent data, a proper structure for the graph is quite critical. This paper proposes a novel model to construct the graph structure for semi-supervised learning. In this new structure, the weights of edges in the graph are obtained by the linear combination of a Nonnegative Sparse graph and K Nearest Neighbour graph (NSKNN-graph). The NSKNN-graph can capture both the global structure (by global sparse graph) and the local structure (by the KNN graph). We demonstrate the effectiveness of NSKNN-graph on the UCI dataset. Experiments show that the NSKNN-graph has advantages over graphs constructed by conventional methods.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yunbin Zhang AU - Chunmei Zhang AU - Qianqi Zhou PY - 2016/04 DA - 2016/04 TI - Nonnegative Sparse and KNN graph for semi-supervised learning BT - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) PB - Atlantis Press SP - 1178 EP - 1182 SN - 2352-5401 UR - https://doi.org/10.2991/ameii-16.2016.223 DO - https://doi.org/10.2991/ameii-16.2016.223 ID - Zhang2016/04 ER -