Abstract
Locally Linear Embedding (LLE) has recently been proposed as a method for dimensional reduction of high-dimensional nonlinear data sets. In LLE each data point is reconstructed from a linear combination of its n nearest neighbors, which are typically found using the Euclidean Distance. We propose an extension of LLE which consists in performing the search for the neighbors with respect to the geodesic distance (ISOLLE). In this study we show that the usage of this metric can lead to a more accurate preservation of the data structure. The proposed approach is validated on both real-world and synthetic data.
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© 2005 Springer-Verlag Berlin Heidelberg
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Varini, C., Degenhard, A., Nattkemper, T. (2005). ISOLLE: Locally Linear Embedding with Geodesic Distance. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_34
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DOI: https://doi.org/10.1007/11564126_34
Publisher Name: Springer, Berlin, Heidelberg
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