Distance Preserving Embeddings for General n-Dimensional Manifolds

By Nakul Verma. 2012

33 citations recorded by the source corpus.

How researchers see this paper

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

1. Dacheng Tao
2. Tongliang Liu
3. K. Jia
4. Shuai Li
5. Raja Giryes
6. Jure Sokolić
7. Guillermo Sapiro
8. Miguel Raul Dias Rodrigues

Citations in the corpus

• Robust Large Margin Deep Neural Networks (2016, background): “The covering number of many structured low-dimensional data models can be bounded in terms of their “intrinsic” properties, for example: a Gaussian mixture model (GMM) with L Gaussians and covariance matrices of rank at most k leads to a covering number N (X ; d, ρ) = L(1 + 2/ρ) [41]; k-sparse signa”
• Orthogonal Deep Neural Networks (2019, background): “Such an assumption is general enough to accommodate at least visual data such as natural images and has been widely used [54].”
• Metric Learning and Manifolds: Preserving the Intrinsic Geometry (2017, methodology): “In Verma (2013) two algorithms are preented, which boldly combine random projections with ideas from the proofs of Nash’s theorem.”