Real numbers, data science and chaos: How to fit any dataset with a single parameter
On 11 May, 2019 By admin 0 Comments
April, 2019
Abstract
We show how any dataset of any modality (time-series, images, sound...) can be approximated by a wellbehaved (continuous, differentiable...) scalar function with a single real-valued parameter. Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data. Targeting an audience of data scientists with a taste for the curious and unusual, the results presented here expand on previous similar observations [1] regarding expressiveness power and generalization of machine learning models.
Resource Type: