Abstract
The aim of this paper is to present a new semi-supervised classification method based on modified Partial Least Squares algorithm and Gaussian Mixture Models. Combining the information contained in unlabeled samples together with the available training labeled samples can increase the classification performance. Our method relies on combining two kernel functions: the standard kernel calculated on data from labeled samples and a generative kernel directly learned by clustering the data. The economical datasets are used to compare the performance of the classification.
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Błaszczyk, P. (2017). Semi-supervised Classification with Modified Kernel Partial Least Squares. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2015. Springer, Singapore. https://doi.org/10.1007/978-981-10-2717-8_8
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DOI: https://doi.org/10.1007/978-981-10-2717-8_8
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