Proceedings of the 2014 International Conference on Computer, Communications and Information Technology

Improved Manifold Learning Algorithm for Data Dimension Reduction Based on KNN

Authors
Liming Liang, Falu Weng, Zhaoyang Chen, Zhen Zhong
Corresponding Author
Liming Liang
Available Online January 2014.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2014 International Conference on Computer, Communications and Information Technology (CCIT 2014)
Part of series
Advances in Intelligent Systems Research
Publication Date
January 2014
ISBN
978-90786-77-97-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/ccit-14.2014.45How to use a DOI?
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  - 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014)
PB  - Atlantis Press
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  -