SVD Algorithms: APEX-like versus Subspace Methods
Neural Processing Letters
ACE4k: An analog I/O 64×64 visual microprocessor chip with 7-bit analog accuracy: Research Articles
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Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
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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.