Improved Manifold Learning Algorithm for Data Dimension Reduction Based on KNN

DOI

10.2991/ccit-14.2014.45How to use a DOI?

Keywords

Manifold learning, weighted norm, dimensionality reduction

Abstract

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.

Copyright

© 2014, 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/).

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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  - 10.2991/ccit-14.2014.45
ID  - Liang2014/01
ER  -