Abstract
Estimating functions on graphs finds well-documented applications in machine learning and, more recently, in signal processing. Given signal values on a subset of vertices, the goal is to estimate the signal on the remaining ones. This task amounts to estimating a function (or signal) over a graph. Most existing techniques either rely on parametric signal models or require costly cross-validation. Leveraging the framework of multi-kernel learning, a data-driven nonparametric approach is developed here. Instead of a single kernel, the algorithm relies on a dictionary of candidate kernels and efficiently selects the most suitable ones by minimizing a convex criterion using a group Lasso module. Numerical tests demonstrate the superior estimation performance of the novel approach over competing alternatives.
Original language | English (US) |
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Title of host publication | 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781467378024 |
DOIs | |
State | Published - Aug 24 2016 |
Event | 19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: Jun 25 2016 → Jun 29 2016 |
Publication series
Name | IEEE Workshop on Statistical Signal Processing Proceedings |
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Volume | 2016-August |
Other
Other | 19th IEEE Statistical Signal Processing Workshop, SSP 2016 |
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Country/Territory | Spain |
City | Palma de Mallorca |
Period | 6/25/16 → 6/29/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Graph kernels
- Kernel regression
- multi-kernel learning