Mixed Type

Mixed-type data, encompassing both continuous and categorical variables, presents unique challenges for machine learning and data analysis. Current research focuses on developing robust clustering and classification methods, often employing information-theoretic approaches, spectral clustering with graph-based representations, and kernel metric learning to handle the diverse data types effectively. These advancements are crucial for improving the performance of various applications, including healthcare (e.g., analyzing electronic health records), drug discovery (e.g., generating molecules), and robotics (e.g., optimizing robot gaits), where mixed-type data is prevalent. Furthermore, significant effort is dedicated to addressing issues like overfitting in self-supervised learning and generating adversarial examples for robust model development.