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, MNIST and CIFAR10 achieving 100% accuracy on all tasks assuming we can determine at test time which probably good optimal scale factor to use for each category.

翻译：在这项工作中,我们调查了在学习背景下签名变换的使用情况。在这个假设下,我们推进了一个监督框架,提供最新的分类准确性,在不需要信用分配的情况下使用极少的标签,且不过分或不过分。我们利用使用签名和日志签名进行和谐分析的工具,并使用RMSE和MAE签名和日志签名作为记分功能。我们开发了一个封闭式方程式方程式,以计算可能的最佳比例系数。分类在CPU级别上进行,比其他方法更快。我们报告了关于AFHQ、MNIST和CIFAR10的所有任务实现100%准确性的结果,假设我们能够在测试时确定每个类别使用的最佳比例系数。