Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data.

In this paper, we propose a data embedding technique for structured data that allows for the direct application of standard vector-based machine learning models without the need for explicit feature extraction. Our approach relies on multiple notions of data proximity, making it suitable for handling mixed data types or incorporating domain knowledge. Our method also reduces the computational costs of pairwise proximity calculations, resulting in improved efficiency and scalability. We demonstrate the effectiveness of our technique on graph and sequence datasets from the biochemical domain. In particular, we show that the method can efficiently be combined with interpretable machine learning approaches like relevance-based learning vector quantization for sophisticated classification learning. • Embedding scheme enabling standard vector-based learning for structured (bio-)data • Savings in computing time, crucial for direct graph or sequence comparison • Allows task-specific mixing of proximity measures, thus avoiding a feature bias • Tailored to shallow machine learning, particularly Learning Vector Quantization • Model interpretability allows evaluation of embedding and proximity mixing schemes [ABSTRACT FROM AUTHOR]