Abstract
Manifold learning problem, aims to seek some directions, which can keep the local structure and neighborhood of each sample as much as possible. In the conventional manifold learning approaches, feature extraction is performed in the original data space. In this paper, a new method called ”the maximized discriminant subspace algorithm” (MDS) is implemented before feature extraction and classification. Extensive experiments show the better classification results than the conventional manifold approaches, due to projecting the original data onto the maximized discriminant subspace in a preliminary phase before feature extraction and classification in the transformed data space.
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Tao, Y., Yang, J. (2013). The Maximized Discriminative Subspace for Manifold Learning Problem. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_95
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DOI: https://doi.org/10.1007/978-3-642-36669-7_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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