Improved Manifold Learning Algorithm for Data Dimension Reduction Based on KNN
Liming Liang, Falu Weng, Zhaoyang Chen, Zhen Zhong
Available Online January 2014.
- https://doi.org/10.2991/ccit-14.2014.45How to use a DOI?
- Manifold learning, weighted norm, dimensionality reduction
- In this paper, a new multi-manifold learning algorithm based on KNN algorithm is proposed in order to provide manifold learning model automatic parameters selection strategy. Basic ideas for such a algorithm is constructing a weighted norm as the variable of the intrinsic low dimensions expression function, and then optimizing the function's variables and getting a automatic selection of the size of the intrinsic low dimensions and the neighborhood in the manifold learning algorithm model. After a series of numerical experiments on simulated and experimental, results proves the feasibility and effectiveness of the algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Liming Liang AU - Falu Weng AU - Zhaoyang Chen AU - Zhen Zhong PY - 2014/01 DA - 2014/01 TI - Improved Manifold Learning Algorithm for Data Dimension Reduction Based on KNN BT - Proceedings of the 2014 International Conference on Computer, Communications and Information Technology PB - Atlantis Press SP - 170 EP - 173 SN - 1951-6851 UR - https://doi.org/10.2991/ccit-14.2014.45 DO - https://doi.org/10.2991/ccit-14.2014.45 ID - Liang2014/01 ER -