A Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams
This work addresses the challenge of incorporating persistent homology into machine learning for protein function prediction, offering an incremental improvement over existing vectorization techniques.
The authors tackled the problem of artificial and ineffective vectorization methods for persistent diagrams in protein function prediction by proposing a geometric vectorization method based on maximal margin classification, which outperformed the best statistical methods in robustness and precision for a binary classification task on proteins.
Persistent homology is an effective method for extracting topological information, represented as persistent diagrams, of spatial structure data. Hence it is well-suited for the study of protein structures. Attempts to incorporate Persistent homology in machine learning methods of protein function prediction have resulted in several techniques for vectorizing persistent diagrams. However, current vectorization methods are excessively artificial and cannot ensure the effective utilization of information or the rationality of the methods. To address this problem, we propose a more geometrical vectorization method of persistent diagrams based on maximal margin classification for Banach space, and additionaly propose a framework that utilizes topological data analysis to identify proteins with specific functions. We evaluated our vectorization method using a binary classification task on proteins and compared it with the statistical methods that exhibit the best performance among thirteen commonly used vectorization methods. The experimental results indicate that our approach surpasses the statistical methods in both robustness and precision.