A general theory of a class of linear neural nets for principal and minor component analysis |
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Authors: | Kiyotoshi Matsuoka |
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Affiliation: | (1) Department of Control Engineering, Kyushu Institute of Technology, Sensui 1-1, 804 Tobata, Kitakyushu, Japan |
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Abstract: | This paper presents a unified theory of a class of learning neural nets for principal component analysis (PCA) and minor component
analysis (MCA). First, some fundamental properties are addressed which all neural nets in the class have in common. Second,
a subclass called the generalized asymmetric learning algorithm is investigated, and the kind of asymmetric structure which
is required in general to obtain the individual eigenvectors of the correlation matrix of a data sequence is clarified. Third,
focusing on a single-neuron model, a systematic way of deriving both PCA and MCA learning algorithms is shown, through which
a relation between the normalization in PCA algorithms and that in MCA algorithms is revealed.
This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January
19–21, 1998 |
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Keywords: | Neural net Principal component analysis Minor component analysis |
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