Riemannian Manifold Learning

By Tong Lin, Hongyuan Zha. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

478 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/81c0b1135741385de2fc99307065f9741edaec86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Marina Meil
2. Dominique C. Perrault-Joncas
3. Francesco Camastra
4. Antonino Staiano
5. Yuanyuan Ren
6. Peng Zhang
7. Sun-Yuan Kung

In Geometry and Topology

1. Marina Meil
2. Dominique C. Perrault-Joncas
3. Francesco Camastra
4. Antonino Staiano
5. Yuanyuan Ren
6. Peng Zhang

Citations in the corpus

• Intrinsic dimension estimation: Advances and open problems (2016, methodology): “Costa-Hero’s algorithm [19], Lin-Zha’s simplex-based method [61], IDEA [81,82] and DANCo [14] algorithms belong to this category.”
• Metric Learning and Manifolds: Preserving the Intrinsic Geometry (2017, background): “The work of (Lin and Zha, 2008) propose a direct approach to global isometry.”
• A new embedding quality assessment method for manifold learning (2011, methodology): “These methods are proved to be useful to isometric manifold learning methods, such as ISOMAP and RML.”