Abstract
Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization. We then compare the bound for those semi-supervised methods to purely supervised methods, and discuss a setting in which the semi-supervised method can only have a constant improvement, ignoring logarithmic terms. By viewing Manifold regularization as a kernel method we then derive Rademacher bounds which allow for a distribution dependent analysis. Finally we illustrate that these bounds may be useful for choosing an appropriate manifold regularization parameter in situations with very sparsely labeled data.
Original language | English |
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Title of host publication | Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings |
Editors | Michael R. Berthold, Ad Feelders, Georg Krempl |
Publisher | SpringerOpen |
Pages | 326-338 |
Number of pages | 13 |
Volume | 12080 |
ISBN (Print) | 9783030445836 |
DOIs | |
Publication status | Published - 2020 |
Event | 18th International Conference on Intelligent Data Analysis, IDA 2020 - Konstanz, Germany Duration: 27 Apr 2020 → 29 Apr 2020 Conference number: 18 |
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 | 12080 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 18th International Conference on Intelligent Data Analysis, IDA 2020 |
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Abbreviated title | IDA 2020 |
Country/Territory | Germany |
City | Konstanz |
Period | 27/04/20 → 29/04/20 |
Other | Virtual/online event due to COVID-19 |
Bibliographical note
Virtual/online event due to COVID-19Keywords
- Learning theory
- Manifold regularization
- Semi-supervised learning