Print Email Facebook Twitter A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization Title A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization Author Mey, A. (TU Delft Interactive Intelligence) Viering, T.J. (TU Delft Pattern Recognition and Bioinformatics) Loog, M. (TU Delft Pattern Recognition and Bioinformatics; University of Copenhagen) Contributor Berthold, Michael R. (editor) Feelders, Ad (editor) Krempl, Georg (editor) Date 2020 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. Subject Learning theoryManifold regularizationSemi-supervised learning To reference this document use: http://resolver.tudelft.nl/uuid:91541fe8-7198-41f5-b56a-79a03f3f1551 DOI https://doi.org/10.1007/978-3-030-44584-3_26 Publisher SpringerOpen ISBN 9783030445836 Source Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings, 12080 Event 18th International Conference on Intelligent Data Analysis, IDA 2020, 2020-04-27 → 2020-04-29, Konstanz, Germany Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12080 LNCS Bibliographical note Virtual/online event due to COVID-19 Part of collection Institutional Repository Document type conference paper Rights © 2020 A. Mey, T.J. Viering, M. Loog Files PDF Mey2020_Chapter_ADistribu ... dIndep.pdf 648.99 KB Close viewer /islandora/object/uuid:91541fe8-7198-41f5-b56a-79a03f3f1551/datastream/OBJ/view