Anderson Acceleration For Bioinformatics-Based Machine Learning
This work addresses a gap in convergence analysis for classical ML models in bioinformatics, though it is incremental as it applies an existing acceleration method to a new context.
The paper tackled the problem of slow convergence in classical machine learning classifiers for tabular data by implementing a support vector machine (SVM) variant with Anderson acceleration, demonstrating that it significantly improves convergence and reduces training loss across several biology datasets.
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.