NeurIPS 1992
Non-Linear Dimensionality Reduction
Abstract
A method for creating a non-linear encoder-decoder for multidimensional data with compact representations is presented. The commonly used technique of autoassociation is extended to allow non-linear representations, and an objec(cid: 173) tive function which penalizes activations of individual hidden units is shown to result in minimum dimensional encodings with respect to allowable error in reconstruction.
Authors
Keywords
No keywords are indexed for this paper.
Context
- Venue
- Annual Conference on Neural Information Processing Systems
- Archive span
- 1987-2025
- Indexed papers
- 30776
- Paper id
- 1049005237860854676