TY - GEN
T1 - Estimating signals over graphs via multi-kernel learning
AU - Romero, Daniel
AU - Ma, Meng
AU - Giannakis, Georgios B.
PY - 2016/8/24
Y1 - 2016/8/24
N2 - 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.
AB - 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.
KW - Graph kernels
KW - Kernel regression
KW - multi-kernel learning
UR - http://www.scopus.com/inward/record.url?scp=84987881624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987881624&partnerID=8YFLogxK
U2 - 10.1109/SSP.2016.7551714
DO - 10.1109/SSP.2016.7551714
M3 - Conference contribution
AN - SCOPUS:84987881624
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PB - IEEE Computer Society
T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Y2 - 25 June 2016 through 29 June 2016
ER -