Fetching the paper…
Reading the bibliography…
We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given.
C. Cortes and V. Vapnik, “Support-vector networks,”
1995
Earlier work this paper cites.
F. R. K. Chung,
1996
Earlier work this paper cites.
C. F. V. Loan, “The ubiquitous Kronecker product,”
2000
Earlier work this paper cites.
R. I. Kondor and J. Lafferty, “Diffusion kernels on graphs and other discrete structures,”
2002
Earlier work this paper cites.
A. J. Smola and R. Kondor,
2003
Earlier work this paper cites.
M. Seeger, “Gaussian processes for machine learning,”
2004
Earlier work this paper cites.
M. Belkin, I. Matveeva, and P. Niyogi, “Tikhonov regularization and semi-supervised learning on large graphs,”
2004
Earlier work this paper cites.
——, “Regularization and semi-supervised learning on large graphs,” in
2004
Earlier work this paper cites.
M. K. Chung and J. Taylor, “Diffusion smoothing on brain surface via finite element method,”
2004
Earlier work this paper cites.
D. Ganesan, B. Greenstein, D. Estrin, J. Heidemann, and R. Govindan, “Multiresolution storage and search in sensor networks,”
2005
Earlier work this paper cites.
A. Argyriou, M. Herbster, and M. Pontil, “Combining graph Laplacians for semi-supervised learning,”
2005
Earlier work this paper cites.
C. E. Rasmussen and C. K. I. Williams,
2005
Earlier work this paper cites.
R. R. Coifman and M. Maggioni, “Diffusion wavelets,”
2006
Earlier work this paper cites.
R. Wagner, V. Delouille, and R. Baraniuk, “Distributed wavelet de-noising for sensor networks,”
2006
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,”
2006
Earlier work this paper cites.
C. M. Bishop,
2006
Earlier work this paper cites.
H. Takeda, S. Farsiu, and P. Milanfar, “Deblurring using regularized locally adaptive kernel regression,”
2008
Earlier work this paper cites.
S. I. Daitch, J. A. Kelner, and D. A. Spielman, “Fitting a graph to vector data,”
2009
Earlier work this paper cites.
Y. Cho and L. K. Saul, “Kernel methods for deep learning,” in
2009
Earlier work this paper cites.
M. K. Chung, P. Bubenik, and P. T. Kim, “Persistence diagrams of cortical surface data,”
2009
Earlier work this paper cites.
C. Cortes, M. Mohri, and A. Rostamizadeh, “L2 regularization for learning kernels,” in
2009
Earlier work this paper cites.
J. Diedrichsen, J. H. Balsters, J. Flavell, E. Cussans, and N. Ramnani, “A probabilistic MR atlas of the human cerebellum,”
2009
Earlier work this paper cites.
S. K. Narang and A. Ortega, “Local two-channel critically sampled filter-banks on graphs,”
2010
Earlier work this paper cites.
S. Seo, M. K. Chung, and H. K. Vorperian, “Heat kernel smoothing using Laplace-Beltrami eigenfunctions,”
2010
Earlier work this paper cites.
P. K. Shivaswamy and T. Jebara,
2010
Earlier work this paper cites.
M. E. J. Newman,
2010
Earlier work this paper cites.
D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,”
2011
Earlier work this paper cites.
N. Leonardi and D. Van De Ville, “Wavelet frames on graphs defined by fMRI functional connectivity,” in
2011
Earlier work this paper cites.
D. Pachauri, C. Hinrichs, M. K. Chung, S. C. Johnson, and V. Singh, “Topology-based kernels with application to inference problems in Alzheimer’s disease,”
2011
Earlier work this paper cites.
D. I. Shuman, B. Ricaud, and P. Vandergheynst, “A windowed graph Fourier transform,”
2012
Earlier work this paper cites.
——, “Perfect reconstruction two-channel wavelet filter banks for graph structured data,”
2012
Earlier work this paper cites.
D. I. Shuman, S. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,”
2013
Earlier work this paper cites.
A. Sandryhaila and J. M. F. Moura, “Discrete signal processing on graphs,”
2013
Earlier work this paper cites.
——, “Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs,”
2013
Earlier work this paper cites.
S. K. Narang, A. Gadde, and A. Ortega, “Signal processing techniques for interpolation in graph structured data,” in
2013
Cited alongside, same era.
C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li, “A graph theoretical regression model for brain connectivity learning of Alzheimer’s disease,”
2013
Cited alongside, same era.
N. Leonardi, J. Richiardi, M. Gschwind, S. Simioni, J.-M. Annoni, M. Schluep, P. Vuilleumier, and D. Van De Ville, “Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest,”
2013
Cited alongside, same era.
——, “Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure,”
2014
Cited alongside, same era.
——, “Discrete signal processing on graphs: Frequency analysis,”
2014
M. Tsitsvero, S. Barbarossa, and P. D. Lorenzo, “Signals on graphs: Uncertainty principle and sampling,”
2016
Later among the works it cites.
A. Anis, A. Gadde, and A. Ortega, “Efficient sampling set selection for bandlimited graph signals using graph spectral proxies,”
2016
Later among the works it cites.
N. Shahid, N. Perraudin, V. Kalofolias, G. Puy, and P. Vandergheynst, “Fast robust pca on graphs,”
2016
Later among the works it cites.
Y. Yankelevsky and M. Elad, “Dual graph regularized dictionary learning,”
2016
Later among the works it cites.
S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, “Stationary graph processes: Nonparametric spectral estimation,”
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
D. Thanou, D. I Shuman, and P. Frossard, “Learning parametric dictionaries for signals on graphs,”
2014
Cited alongside, same era.
D. Thanou, D. I. Shuman, and P. Frossard, “Learning parametric dictionaries for signals on graphs,”
2014
Cited alongside, same era.
G.-B. Huang, “An insight into extreme learning machines: Random neurons, random features and kernels,”
2014
Cited alongside, same era.
A. Venkitaraman, S. Chatterjee, and P. Handel, “On Hilbert transform of signals on graphs,”
2015
Cited alongside, same era.
N. Tremblay and P. Borgnat, “Joint filtering of graph and graph-signals,”
2015
Cited alongside, same era.
S. Chen, R. Varma, A. Sandryhaila, and J. Kovacevic, “Discrete signal processing on graphs: Sampling theory,”
2015
Cited alongside, same era.
H. Q. Nguyen and M. N. Do, “Downsampling of signals on graphs via maximum spanning trees,”
2015
Cited alongside, same era.
2016
Later among the works it cites.
X. Dong, D. Thanou, P. Frossard, and P. Vandergheynst, “Learning Laplacian matrix in smooth graph signal representations,”
2016
Later among the works it cites.
S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, “Reconstruction of graph signals through percolation from seeding nodes,”
2016
Later among the works it cites.
——, “Estimating signals over graphs via multi-kernel learning,” in
2016
Later among the works it cites.
V. N. Ioannidis, D. Romero, and G. B. Giannakis, “Kernel-based reconstruction of space-time functions via extended graphs,” in
2016
Later among the works it cites.
V. Kalofolias, “How to learn a graph from smooth signals,”
2016
Later among the works it cites.
H. Behjat, U. Richter, D. Van De Ville, and L. Sörnmo, “Signal-adapted tight frames on graphs,”
2016
Later among the works it cites.
O. Teke and P. P. Vaidyanathan, “Extending classical multirate signal processing theory to graphs-part I: Fundamentals,”
2017
Closest in time.
——, “Extending classical multirate signal processing theory to graphs-part II: M-channel filter banks,”
2017
Closest in time.
S. P. Chepuri and G. Leus, “Graph sampling for covariance estimation,”
2017
Closest in time.
A. G. Marques, S. Segarra, G. Leus, and A. Ribeiro, “Stationary graph processes and spectral estimation,”
2017
Closest in time.
N. Perraudin and P. Vandergheynst, “Stationary signal processing on graphs,”
2017
Closest in time.
P. Berger, G. Hannak, and G. Matz, “Graph signal recovery via primal-dual algorithms for total variation minimization,”
2017
Closest in time.
Y. Shen, B. Baingana, and G. B. Giannakis, “Tensor decompositions for identifying directed graph topologies and tracking dynamic networks,”
2017
Closest in time.
S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, “Network topology inference from spectral templates,”
2017
Closest in time.
S. P. Chepuri, S. Liu, G. Leus, and A. Hero, “Learning sparse graphs under smoothness prior,”
2017
Closest in time.
B. Pasdeloup, V. Gripon, G. Mercier, D. Pastor, and M. G. Rabbat, “Characterization and inference of graph diffusion processes from observations of stationary signals,”
2017
Closest in time.
H. Dou, D. Ming, Z. Yang, Z. Pan, Y. Li, and J. Tian, “Object-based visual saliency via Laplacian regularized kernel regression,”
2017
Closest in time.
D. Romero, M. Ma, and G. B. Giannakis, “Kernel-based reconstruction of graph signals,”
2017
Closest in time.
D. Romero, V. N. Ioannidis, and G. B. Giannakis, “Kernel-based reconstruction of space-time functions on dynamic graphs,”
2017
Closest in time.
Y. Shen, B. Baingana, and G. B. Giannakis, “Kernel-based structural equation models for topology identification of directed networks,”
2017
Closest in time.
L. F. O. Chamon and A. Ribeiro, “Greedy sampling of graph signals,”
2018
Closest in time.
2018
Closest in time.
A. Ortega, P. Frossard, J. Kovačević, J. M. F. Moura, and P. Vandergheynst, “Graph signal processing: Overview, challenges, and applications,”
2018
Closest in time.
V. Solo, J. Poline, M. A. Lindquist, S. L. Simpson, F. D. Bowman, M. K. Chung, and B. Cassidy, “Connectivity in fMRI: Blind spots and breakthroughs,”
2018
Closest in time.
V. N. Ioannidis, M. Ma, A. N. Nikolakopoulos, G. B. Giannakis, and D. Romero,
2018
Closest in time.
A. Venkitaraman, S. Chatterjee, and P. Händel, “Gaussian processes over graphs,”
2018
Closest in time.
A. Venkitaraman, S. Chatterjee, and P. Händel, “Multi-kernel regression for graph signal processing,” in
2018
Closest in time.
A. Venkitaraman, S. Chatterjee, and P. Händel, “On Hilbert transform, analytic signal, and modulation analysis for signals over graphs,”
2019
Closest in time.