Abstract
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others. In this work, we estimate log determinants under the framework of maximum entropy, given information in the form of moment constraints from stochastic trace estimation. The estimates demonstrate a significant improvement on state-of-the-art alternative methods, as shown on a wide variety of matrices from the SparseSuite Matrix Collection. By taking the example of a general Markov random field, we also demonstrate how this approach can significantly accelerate inference in large-scale learning methods involving the log determinant.
Original language | English (US) |
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Title of host publication | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings |
Editors | Michelangelo Ceci, Saso Dzeroski, Celine Vens, Ljupco Todorovski, Jaakko Hollmen |
Publisher | Springer Verlag |
Pages | 323-338 |
Number of pages | 16 |
ISBN (Print) | 9783319712482 |
DOIs | |
State | Published - 2017 |
Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of Duration: Sep 18 2017 → Sep 22 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10534 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 |
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Country/Territory | Macedonia, The Former Yugoslav Republic of |
City | Skopje |
Period | 09/18/17 → 09/22/17 |
Bibliographical note
Publisher Copyright:© 2017, Springer International Publishing AG.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science