Abstract
This paper proposes an out-of-sample semi-supervised feature extractor that can be used for classification tasks. We propose a flexible semi-supervised feature extraction method having an out-of-sample extension. It seeks a non-linear subspace that is close to a linear one. The proposed method relies on criterion that simultaneously exploits the discrimination information provided by the labeled samples, preserve the graph-based smoothness, and minimizes the discrepancy between the unknown linear regression and the unknown non-linear embedding. We provide experiments on three benchmark databases in order to study the performance of the proposed method. These experiments demonstrate much improvement over the state-of-the-art algorithms that are either based on label propagation or semi-supervised graph-based embedding.
This work is partially supported by the project EHU13/40.
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Dornaika, F., El Traboulsi, Y., Cases, B., Assoum, A. (2014). Image Classification via Semi-supervised Feature Extraction with Out-of-Sample Extension. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_18
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DOI: https://doi.org/10.1007/978-3-319-14249-4_18
Publisher Name: Springer, Cham
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