Code Supplement for "Local Conformal Autoencoder for standardized data coordinates"
Abstract/Contents
- Abstract
In this code supplement to the paper "LOCA: LOcal Conformal Autoencoder for standardized data coordinates" we offer a Python software library that includes:
- Implementation of the LOCA algorithm
- Scripts that generate each of the figures in this paper.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | August 2020 |
Creators/Contributors
| Author | Peterfreund, Erez |
|---|---|
| Author | Lindenbaum, Ofir |
| Author | Gavish, Matan |
| Author | Bertalan, Tom |
Subjects
| Subject | Manifold learning |
|---|---|
| Subject | autoencoder |
| Subject | dimensionality reduction |
| Subject | canonical coordiantes |
Bibliographic information
| Related Publication |
E. Peterfreund, O. Lindenbaum, F. Dietrich, T. Bertalan, M. Gavish, I. G.
|
|---|---|
| Related item |
|
| Location | https://purl.stanford.edu/zt044bg9296 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
Code Supplement for "Local Conformal
Autoencoderfor standardized data coordinates" by Peterfreund, E., Lindenbaum, O., Dietrich, F., Bertalanc, T., Gavish, M., Kevrekidisc, I. G., and Coifman, R.R. (2020)
Available online at https://purl.stanford.edu/zt044bg9296
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- gavish@stanford.edu
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