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Non-Linear Dimensionality Reduction

Conference Paper Artificial Intelligence ยท Machine Learning

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.

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Keywords

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
1049005237860854676
v2026.09.13