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Calmon, Flavio P., Makhdoumi, Ali, Medard, Muriel, Varia, Mayank, Christiansen, Mark M. and Duffy, Ken R. (2017) Principal Inertia Components and Applications. IEEE Transactions on Information Theory, 63 (8). pp. 5011-5038. ISSN 1557-9654
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Abstract
We explore properties and applications of the principal inertia components (PICs) between two discrete random variables X and Y. The PICs lie in the intersection of information and estimation theory, and provide a fine-grained decomposition of the dependence between X and Y. Moreover, the PICs describe which functions of X can or cannot be reliably inferred (in terms of MMSE), given an observation of Y. We demonstrate that the PICs play an important role in information theory, and they can be used to characterize information-theoretic limits of certain estimation problems. In privacy settings, we prove that the PICs are related to the fundamental limits of perfect privacy
| Item Type: | Article |
|---|---|
| Additional Information: | This is the preprint version (arXiv:1704.00820) of the published article, which is available at DOI: 10.1109/TIT.2017.2700857 |
| Keywords: | Principal inertia components; estimation; privacy; minimum mean square error; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 10168 |
| Identification Number: | 10.1109/TIT.2017.2700857 |
| Depositing User: | Dr Ken Duffy |
| Date Deposited: | 02 Nov 2018 14:57 |
| Journal or Publication Title: | IEEE Transactions on Information Theory |
| Publisher: | IEEE |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/10168 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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