Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-means Algorithm
- 10.2991/iccsee.2013.599How to use a DOI?
- training data reduction, classification, nonlinear feature extraction, greedy kernel principal component analysis, fuzzy C-means algorithm, kernel matrix
Nonlinear feature extraction used standard Kernel Principal Component Analysis (KPCA) method has large memories and high computational complexity in large datasets. A Greedy Kernel Principal Component Analysis (GKPCA) method is applied to reduce training data and deal with the nonlinear feature extraction problem for training data of large data in classification. First, a subset, which approximates to the original training data, is selected from the full training data using the greedy technique of the GKPCA method. Then, the feature extraction model is trained by the subset instead of the full training data. Finally, FCM algorithm classifies feature extraction data of the GKPCA, KPCA and PCA methods, respectively. The simulation results indicate that the feature extraction performance of both the GKPCA, and KPCA methods outperform the PCA method. In addition of retaining the performance of the KPCA method, the GKPCA method reduces computational complexity due to the reduced training set in classification.
- © 2013, 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 - Xiaofang Liu AU - Chun Yang PY - 2013/03 DA - 2013/03 TI - Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-means Algorithm BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 2393 EP - 2396 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.599 DO - 10.2991/iccsee.2013.599 ID - Liu2013/03 ER -