Dissimilarity Measure

Dissimilarity measures quantify the difference between data points or distributions, serving as fundamental building blocks in various machine learning tasks like clustering, classification, and alignment. Current research focuses on developing robust and efficient dissimilarity measures tailored to specific data types (e.g., time series, images, text) and addressing challenges such as computational scalability, outlier sensitivity, and the impact of data heterogeneity. These advancements are crucial for improving the accuracy and interpretability of machine learning models across diverse scientific domains and practical applications, including bioinformatics, image retrieval, and geospatial analysis.