Learning with Signatures

• URL: http://arxiv.org/abs/2204.07953v1
• Date: Sun, 17 Apr 2022 08:36:15 GMT
• Title: Learning with Signatures
• Authors: J. de Curt\`o and I. de Zarz\`a and Carlos T. Calafate and Hong Yan
• Abstract summary: We advance a supervised framework that provides state-of-the-art classification accuracy with the use of very few labels without the need of credit assignment and with minimal or no overfitting. We leverage tools from harmonic analysis by the use of the signature and log-signature and use as a score function RMSE and MAE Signature and log-signature.
• Score: 8.569235370614145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we investigate the use of the Signature Transform in the context of Learning. Under this assumption, we advance a supervised framework that provides state-of-the-art classification accuracy with the use of very few labels without the need of credit assignment and with minimal or no overfitting. We leverage tools from harmonic analysis by the use of the signature and log-signature and use as a score function RMSE and MAE Signature and log-signature. We develop a closed-form equation to compute probably good optimal scale factors. Classification is performed at the CPU level orders of magnitude faster than other methods. We report results on AFHQ dataset, Four Shapes, MNIST and CIFAR10 achieving 100% accuracy on all tasks.

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