Searching for "Authors response to 'A comment on "Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding"'." – sorted by Relevance.
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Isometric projection
- Projection constructs a weighted data graph where the weights are discrete approximations of the geodesic
- Cited by 2 (2 self) – Add To MetaCart
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Isometric Embedding and Continuum ISOMAP
- ]. In particular, 1) a so-called neighborhood graph G of the data points x i 's is constructed with an edge
- Cited by 15 (1 self) – Add To MetaCart
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The application of extended geodesic distance in head poses estimation
- to compute the geodesic distances for the head-pose estimation, which is necessary in a variety
- Cited by 1 (0 self) – Add To MetaCart
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Graph Approximations to Geodesics on Embedded Manifolds
- only the data points. A crucial stage in the algorithm involves estimating the unknown geodesic
- Cited by 53 (2 self) – Add To MetaCart
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Distance Functions and Geodesics on Points Clouds
- of computationally optimal algorithms for computing distance functions in Cartesian grids. We then use
- Cited by 10 (3 self) – Add To MetaCart
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Non-Isometric Manifold Learning: Analysis and an Algorithm
- online. The embedding methods require a fully connected neighborhood graph; we simply discarded data
- Cited by 2 (0 self) – Add To MetaCart
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On isometric embedding of facial surfaces into S 3
- in that space. Presented here is a discussion on isometric embedding into S 3 , which appears to be superior
- Cited by 4 (2 self) – Add To MetaCart
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Neighborhood preserving embedding
- Neighborhood Preserving Embedding. It is a linear approximation to Locally Linear Embedding [11]. As a result
- Cited by 11 (4 self) – Add To MetaCart
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Geodesic entropic graphs for dimension and entropy estimation in manifold learning
- Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning Jose A. Costa
- Cited by 31 (2 self) – Add To MetaCart
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Global Optimization Using Embedded Graphs
- Global Optimization Using Embedded Graphs HIROSHI ISHIKAWA A dissertation submitted in partial
- Cited by 19 (0 self) – Add To MetaCart

