Related papers: Learning from few examples with nonlinear feature maps

Learning from few examples with nonlinear feature maps

URL: http://arxiv.org/abs/2203.16935v1
Date: Thu, 31 Mar 2022 10:36:50 GMT
Title: Learning from few examples with nonlinear feature maps
Authors: Ivan Y. Tyukin, Oliver Sutton, Alexander N. Gorban
Abstract summary: We explore the phenomenon and reveal key relationships between dimensionality of AI model's feature space, non-degeneracy of data distributions, and the model's generalisation capabilities. The main thrust of our present analysis is on the influence of nonlinear feature transformations mapping original data into higher- and possibly infinite-dimensional spaces on the resulting model's generalisation capabilities.
Score: 68.8204255655161
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In this work we consider the problem of data classification in post-classical settings were the number of training examples consists of mere few data points. We explore the phenomenon and reveal key relationships between dimensionality of AI model's feature space, non-degeneracy of data distributions, and the model's generalisation capabilities. The main thrust of our present analysis is on the influence of nonlinear feature transformations mapping original data into higher- and possibly infinite-dimensional spaces on the resulting model's generalisation capabilities. Subject to appropriate assumptions, we establish new relationships between intrinsic dimensions of the transformed data and the probabilities to learn successfully from few presentations.

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