Abstract
Feature extraction methods are widely employed to reduce dimensionality of data and enhance the discriminative information. Among the methods, manifold learning approaches have been developed to detect the underlying manifold structure of the data based on local invariants, which are usually guaranteed by an adjacent graph of the sampled data set. The performance of the manifold learning approaches is however affected by the locality of the data, i.e. what is the neighborhood size for suitably representing the locality? In this paper, we address this issue through proposing a method to adaptively select the neighborhood size. It is applied to the manifold learning approach Locality Preserving Projections (LPP) which is a popular linear reduction algorithm. The effectiveness of the adaptive neighborhood selection method is evaluated by performing classification and clustering experiments on the real-life data sets.
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Acknowledgements
This work was supported partly by the National Natural Science Foundation of China under Grant 61573137; Zhejiang Provincial Natural Science Foundation under Grants LY13F020011, LY14F010010 and LY14F020009.
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Hu, W., Cheng, X., Jiang, Y., Choi, KS., Lou, J. (2017). Locality Preserving Projections with Adaptive Neighborhood Size. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_21
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DOI: https://doi.org/10.1007/978-3-319-63309-1_21
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