快速计算序列数据的签名核，支持GPU加速和机器学习任务。
A User's Guide to $\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel
- 基于张量压缩的新算法，提升签名核计算效率。
- 兼容Scikit-Learn，支持下游学习任务的端到端使用。
- 专为时间序列设计，适合需要高效核方法的研究者。
签名核是一种针对序列与时间数据的正定核，在机器学习中因强大的理论保障、优异的实证表现及近期可扩展变体而日益流行。本章简要介绍$ exttt{KSig}$——一个与$ exttt{Scikit-Learn}$兼容的Python包，实现了多种基于GPU加速的签名核计算算法及下游学习任务。我们还提出一种基于张量压缩的新算法，相比现有方法表现出更优性能。该工具包可在https://github.com/tgcsaba/ksig获取。
原文摘要 · Abstract (English)
The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introduction to $\texttt{KSig}$, a $\texttt{Scikit-Learn}$ compatible Python package that implements various GPU-accelerated algorithms for computing signature kernels, and performing downstream learning tasks. We also introduce a new algorithm based on tensor sketches which gives strong performance compared to existing algorithms. The package is available at https://github.com/tgcsaba/ksig.
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