Research on Image Dimension Reduction Algorithm Based Manifold Learning

DOI

10.2991/icsnce-16.2016.47How to use a DOI?

Keywords

Manifold learning; Feature extraction; Intrinsic dimensionality; Data mining

Abstract

For 1D and 2D image feature extraction ignore the structural information of the image, resulting the loss of recognition accuracy, the feature extraction of 3D and multiplanar images while considering the data structure with each other, but the curse of dimensionality increases the computational complexity. Using manifold learning, embedding stable manifold into the original data space, so that the multidimensional data in the feature data is mapped to the manifold, discovered that the low dimensional structure hidden in high dimensional data which people unable to perceive, and then under the premise of without losing the data information, reduce the dimension of the raw data, so as to reduce the computational complexity.

Copyright

© 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/).

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TY  - CONF
AU  - Yuanshao Hou
AU  - Yao Zhang
PY  - 2016/07
DA  - 2016/07
TI  - Research on Image Dimension Reduction Algorithm Based Manifold Learning
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 242
EP  - 246
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.47
DO  - 10.2991/icsnce-16.2016.47
ID  - Hou2016/07
ER  -