Non-linear Dimensionality Reduction by Locally Linear Isomaps
By Ashutosh Saxena, Abhinav Kumar Gupta, Amitabha Mukerjee. 2004
73 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefano Ermon
- Aditya Grover
- L. V. D. van der Maaten
- Sergey Levine
- Jaap van den Herik
- Wei Chen
- Ramin Bostanabad
- Wing Kam Liu
In Computer Vision and Pattern Recognition
- Stefano Ermon
- Aditya Grover
- Luc Van Gool
- Trevor Darrell
- Ming-Hsuan Yang
- Dacheng Tao
- Jitendra Malik
- Xiaogang Wang
Citations in the corpus
- Graphite: Iterative Generative Modeling of Graphs (2018, methodology): “Popular ones include Laplacian Eigenmaps [2] and IsoMaps [36].”
- Dimensionality Reduction: A Comparative Review (2008, methodology): “, by removing datapoints with large total flows in the shortest-path algorithm [27] or by removing nearest neighbors that violate local linearity of the neighborhood graph [96].”
- Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, background): “Examples of this set of approaches include Isomap and its variants [209,210].”