Abstract
Linear Discriminant Analysis (LDA) is a commonly used method for dimensionality reduction, which preserves class separability. Despite its successes, it has limitations under some situations, including the small sample size problem. In practice, when the training data set is small, the covariance matrix of each class may not be accurately estimated. Moreover, LDA doesn’t handle unlabeled data. In this paper, we propose a semi-supervised method called Discriminative Semi-supervised Learning in Manifold subspace (DSLM), which aims at overcoming all these limitations. The proposed method is designed to explore the discriminative information of labeled data and to preserve the intrinsic geometric structure of the data. We empirically compare our method with several related methods on face databases. Results are obtained from the experiments showing the effectiveness of our proposed method .
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This research is supported by research funding from Science Research funding (T-2015.21) and Honors Program, University of Science, Vietnam National University - Ho Chi Minh City.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Vo, TM.D., Truong, H.P., Le, T.H. (2016). Discriminative Semi-supervised Learning in Manifold Subspace for Face Recognition. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_24
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DOI: https://doi.org/10.1007/978-3-319-29236-6_24
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