Abstract
This article presents an online learning method for modeling high dimensional input data. This method approximates a nonlinear function by summing up several local linear functions. Each linear function is represented as the weighted sum of a small number of dominant variables, which are extracted by the partial least squares (PLS) regression method. Moreover, a radial function, which represents the respective input area of each linear function, is also redefined using the dominant variables. This article also presents an online deterministic annealing expectation maximization (DAEM) algorithm which includes a temperature control mechanism for acquireing the most suitable system parameters. Experimental results show the effective learning behavior of the new method.
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© 2008 Springer-Verlag Berlin Heidelberg
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Hayami, J., Yamauchi, K. (2008). PLS Mixture Model for Online Dimension Reduction. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_30
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DOI: https://doi.org/10.1007/978-3-540-69158-7_30
Publisher Name: Springer, Berlin, Heidelberg
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