Abstract
This paper presents a linear replicator [2][4] based on minimizing the reconstruction error [8][9]. It can be used to study the learning behaviors of the kernel principal component analysis [10], the Hebbian algorithm for the principle component analysis (PCA) [8][9] and the iterative kernel PCA [3].
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Cheng, WC., Liou, CY. (2010). Linear Replicator in Kernel Space. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_10
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DOI: https://doi.org/10.1007/978-3-642-13318-3_10
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