Local Intrinsic Dimensional Entropy

arXiv2304.02223MaRDI QIDQ6432123

Author name not available (Why is that?)

Publication date: 5 April 2023

Abstract: Most entropy measures depend on the spread of the probability distribution over the sample space $mathcalX$, and the maximum entropy achievable scales proportionately with the sample space cardinality $|mathcalX|$. For a finite $|mathcalX|$, this yields robust entropy measures which satisfy many important properties, such as invariance to bijections, while the same is not true for continuous spaces (where $|mathcalX|=infty$). Furthermore, since $mathbbR$ and $mathbb{R}^d$ ($dinmathbb{Z}^{+}$) have the same cardinality (from Cantor's correspondence argument), cardinality-dependent entropy measures cannot encode the data dimensionality. In this work, we question the role of cardinality and distribution spread in defining entropy measures for continuous spaces, which can undergo multiple rounds of transformations and distortions, e.g., in neural networks. We find that the average value of the local intrinsic dimension of a distribution, denoted as ID-Entropy, can serve as a robust entropy measure for continuous spaces, while capturing the data dimensionality. We find that ID-Entropy satisfies many desirable properties and can be extended to conditional entropy, joint entropy and mutual-information variants. ID-Entropy also yields new information bottleneck principles and also links to causality. In the context of deep learning, for feedforward architectures, we show, theoretically and empirically, that the ID-Entropy of a hidden layer directly controls the generalization gap for both classifiers and auto-encoders, when the target function is Lipschitz continuous. Our work primarily shows that, for continuous spaces, taking a structural rather than a statistical approach yields entropy measures which preserve intrinsic data dimensionality, while being relevant for studying various architectures.

Has companion code repository: https://github.com/kentridgeai/id-entropy

This page was built for publication: Local Intrinsic Dimensional Entropy

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6432123)