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Fig. 4 | Journal of Big Data

Fig. 4

From: Learning manifolds from non-stationary streams

Fig. 4

Procrustes error (PE) between the ground truth with a GP-Isomap (blue line) with the geodesic distance based kernel, b S-Isomap (dashed blue line with dots) and c GP-Isomap (green line) using the Euclidean distance based kernel, for different fractions (f) of data used in the batch \({\mathcal {B}}\). The behavior of PE for a closely matches that for b. However, the PE for GP-Isomap using the Euclidean distance kernel remains high irrespective of f demonstrating its unsuitability for manifolds

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