Abstract
Fisher Discriminant Analysis (FDA) is a popular method for dimensionality reduction. Local Fisher Discriminant Analysis (LFDA) is an improvement of FDA, which can preserve the local structures of the feature space in multi-class cases. However, the affinity matrix in LFDA cannot reflect the actual interrelationship among all the neighbors for each sample point. In this paper, we propose a new LFDA approach with the affinity matrix being solved by the locally linear embedding (LLE) method to preserve the particular local structures of the specific feature space. Moreover, for nonlinear cases, we extend this new LFDA method to the kernelized version by using the kernel trick. It is demonstrated by the experiments on five real-world datasets that our proposed LFDA methods with LLE affinity matrix are applicable and effective.
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Zhao, Y., Ma, J. (2013). Local Fisher Discriminant Analysis with Locally Linear Embedding Affinity Matrix. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_57
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DOI: https://doi.org/10.1007/978-3-642-39065-4_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
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