Abstract
Dimensionality Reduction is a widely used method of removing redundant features and data compression. Dimensionality reduction usually occurs in a supervised setting in which all the samples are labelled. However, aspects of spectral clustering and semi-supervised learning can be used in Dimensionality reduction to ensure minimum loss of important data while projecting the high-dimensional data into lower dimensions. We have proposed a novel framework called Semi-supervised Regularized Co-planar Discriminant Analysis (SRCDA) that creates a graph of labelled and unlabelled data and uses label propagation to predict the classes of the unlabelled data. Additionally, we introduce a regularized term which is used to prevent overfitting. The proposed algorithm is evaluated against several other state-of-the-art algorithms with benchmark datasets including PIE Face, ORL and Yale Dataset. The proposed algorithm shows higher accuracies compared to the other algorithms and can be used in real-life datasets where the unlabelled data is vastly greater than the labelled samples. We have also conducted a statistical significance test to verify the results obtained.
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Sanodiya, R.K., Thalakottur, M.D., Mathew, J., Khushi, M. (2019). Semi-supervised Regularized Coplanar Discriminant Analysis. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_22
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DOI: https://doi.org/10.1007/978-3-030-36802-9_22
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