Indian buffet process

The Indian Buffet Process is a mathematical model used to represent situations where items can have multiple features that are not fixed in number. Imagine a buffet where guests (data points) choose various dishes (features). Each guest can select any combination of dishes, and new dishes can be introduced as the buffet grows. This process allows for flexible, probabilistic modeling of hidden or latent features in data, especially when the number of features is unknown beforehand. It’s useful in machine learning for uncovering underlying structures in complex datasets without specifying the total number of features in advance.