Learning Diverse and Discriminative Representations via the Principle of
Maximal Coding Rate Reduction
Learning Diverse and Discriminative Representations via the Principle of
Maximal Coding Rate Reduction
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{MCR}^2$), an information-theoretic measure that maximizes the coding rate difference between the whole dataset and the sum of each individual class. We clarify its relationships with most existing …