ISOLLE: Locally linear embedding with geodesic distance

Varini C, Degenhard A, Nattkemper TW (2005)
In: KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005. 3721. SPRINGER-VERLAG BERLIN: 331-342.

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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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Varini C, Degenhard A, Nattkemper TW. ISOLLE: Locally linear embedding with geodesic distance. In: KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005. Vol 3721. SPRINGER-VERLAG BERLIN; 2005: 331-342.
Varini, C., Degenhard, A., & Nattkemper, T. W. (2005). ISOLLE: Locally linear embedding with geodesic distance. KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005, 3721, 331-342.
Varini, C., Degenhard, A., and Nattkemper, T. W. (2005). “ISOLLE: Locally linear embedding with geodesic distance” in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005, vol. 3721, (SPRINGER-VERLAG BERLIN), 331-342.
Varini, C., Degenhard, A., & Nattkemper, T.W., 2005. ISOLLE: Locally linear embedding with geodesic distance. In KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005. no.3721 SPRINGER-VERLAG BERLIN, pp. 331-342.
C. Varini, A. Degenhard, and T.W. Nattkemper, “ISOLLE: Locally linear embedding with geodesic distance”, KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005, vol. 3721, SPRINGER-VERLAG BERLIN, 2005, pp.331-342.
Varini, C., Degenhard, A., Nattkemper, T.W.: ISOLLE: Locally linear embedding with geodesic distance. KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005. 3721, p. 331-342. SPRINGER-VERLAG BERLIN (2005).
Varini, C, Degenhard, A, and Nattkemper, Tim Wilhelm. “ISOLLE: Locally linear embedding with geodesic distance”. KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005. SPRINGER-VERLAG BERLIN, 2005.Vol. 3721. 331-342.
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