164765
20190812205509.0
CONF
Semi-Supervised Learning with Spectral Graph Wavelets
2011
2011
Conference Papers
We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spectral graph wavelets behave like discrete multiscale differential operators on graphs, and thus can sparsely approximate piecewise smooth signals. Therefore, rather than enforce a prior belief that the labels are globally smooth with respect to the intrinsic structure of the graph, we enforce sparse priors on the spectral graph wavelet coefficients. One issue that arises when the proportion of data with labels is low is that the fine scale wavelets that are useful in sparsely representing discontinuities are largely masked, making it difficult to recover the high frequency components of the label sequence. We discuss this challenge, and propose one method to use the structured sparsity of the wavelet coefficients to aid label reconstruction.
Sparse approximation
spectral graph theory
structured sparsity
transductive regression
wavelets
242930
Shuman, David I.
201233
245594
Faraji, Mohammadjavad
191591
240428
Vandergheynst, Pierre
120906
International Conference on Sampling Theory and Applications (SampTA)
Singapore
May 2-6, 2011
Proceedings of the International Conference on Sampling Theory and Applications (SampTA)
283801
http://infoscience.epfl.ch/record/164765/files/Shuman_et_al_SAMPTA_2011.pdf
Preprint
Preprint
252393
LTS4
U10851
252392
LTS2
U10380
oai:infoscience.tind.io:164765
STI
conf
GLOBAL_SET
201233
120906
253580
EPFL-CONF-164765
EPFL
REVIEWED
PUBLISHED
CONF