Unsupervised Learning of Curved Manifolds
作者： Vin de Silva , Joshua B. Tenenbaum
DOI: 10.1007/978-0-387-21579-2_31
关键词: Isomap 、 Unsupervised learning 、 Nonlinear dimensionality reduction 、 Regular polygon 、 Mathematics 、 Pattern recognition 、 Data set 、 Class (set theory) 、 Conformal map 、 Artificial intelligence 、 Range (mathematics)
摘要: We describe a variant of the Isomap manifold learning algorithm [1], called ‘C-Isomap’. was designed to learn non-linear mappings which are isometric embeddings flat, convex data set. C-Isomap is recover in larger class conformal embeddings, provided that original sampling density reasonably uniform. compare performance both versions and other algorithms for (MDS, LLE, GTM) on range sets.
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