Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2009.5179013
Title: A Cellular Neural Network as a Principal Component analyzer
Authors: Haung, C.-H.
Leow, W.-K. 
Racoceanu, D. 
Issue Date: 2009
Source: Haung, C.-H.,Leow, W.-K.,Racoceanu, D. (2009). A Cellular Neural Network as a Principal Component analyzer. Proceedings of the International Joint Conference on Neural Networks : 1163-1170. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2009.5179013
Abstract: In this paper, A configuration of Cellular Neural Network (CNN) is introduced to implement Principal Component Analysis (PCA). CNN is a parallel computing paradigm. Many researchers considered it as the next generation universal machine and developed so-called CNN universal chips. Based on the capability of CNN, an alternative PCA implementation named Principal Component Analyzing Cellular Neural Network (PCACNN) is proposed. PCA is used to reduce the dimensions of a given dataset in order to extract the principal information of the given dataset. In decades, many researchers presented their investigations based on PCA in order to improve the performance and/or to attack some open issues in specific fields. In this paper, PCA is implemented based on the architecture and capabilities of CNN. Consequently, the computing performance of PCA can be improved as long as the CNN architecture can be realized. © 2009 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/40477
ISBN: 9781424435531
DOI: 10.1109/IJCNN.2009.5179013
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