Anderson Acceleration For Bioinformatics-Based Machine Learning

TL;DR

This paper investigates the application of Anderson acceleration to support vector machine classifiers in bioinformatics, demonstrating improved convergence and training efficiency on biological datasets.

Contribution

It introduces a novel SVM variant incorporating Anderson acceleration and evaluates its effectiveness, filling a research gap in classical machine learning convergence analysis.

Findings

AA significantly improves convergence speed.

AA reduces training loss over iterations.

Demonstrates potential of AA in bioinformatics classifiers.

Abstract

Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimization. Despite its popularity in these areas, the effectiveness of AA in classical machine learning classifiers has not been thoroughly studied. Tabular data, in particular, presents a unique challenge for deep learning models, and classical machine learning models are known to perform better in these scenarios. However, the convergence analysis of these models has received limited attention. To address this gap in research, we implement a support vector machine (SVM) classifier variant that incorporates AA to speed up convergence. We evaluate the performance of our SVM with and without Anderson acceleration on several datasets from the biology domain and demonstrate that the use of AA significantly improves convergence and reduces the training loss as the number of iterations increases. Our findings provide a promising perspective on the potential of Anderson acceleration in the training of simple machine learning classifiers and underscore the importance of further research in this area. By showing the effectiveness of AA in this setting, we aim to inspire more studies that explore the applications of AA in classical machine learning.