Kernel methods library for pattern analysis and machine learning in python

TL;DR

The paper introduces a Python library called kernelmethods that facilitates the use of kernel techniques for pattern analysis and machine learning across diverse data types, emphasizing flexibility, efficiency, and ease of customization.

Contribution

It provides a comprehensive, domain-agnostic Python library for kernel methods, enabling easy use and customization for various data types and large-scale datasets.

Findings

Library supports numerical, categorical, and graph data types.

Enables efficient kernel operations for large datasets.

Facilitates domain adaptation and interoperability.

Abstract

Kernel methods have proven to be powerful techniques for pattern analysis and machine learning (ML) in a variety of domains. However, many of their original or advanced implementations remain in Matlab. With the incredible rise and adoption of Python in the ML and data science world, there is a clear need for a well-defined library that enables not only the use of popular kernels, but also allows easy definition of customized kernels to fine-tune them for diverse applications. The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion, allowing the sample data type to be anything from numerical, categorical, graphs or a combination of them. In addition, this library provides a number of well-defined classes to make various kernel-based operations efficient (for large scale datasets), modular (for ease of domain adaptation), and inter-operable (across different ecosystems). The library is available at https://github.com/raamana/kernelmethods.