 Maximally informative dimensions

Maximally Informative Dimensions (MID) refers to a concept in the fields of data science and machine learning, particularly in the context of dimensionality reduction and feature selection. It focuses on identifying the dimensions (or features) of a dataset that provide the most useful information for a particular task, such as classification, regression, or clustering. The underlying idea of maximally informative dimensions is that not all dimensions in a dataset contribute equally to the predictive power or understanding of the data.