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Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression.
G. Nemhauser, L. Wolsey, and M. Fisher, “An analysis of approximations for maximizing submodular set functions—I,” Mathematical Programming , vol. 14[1], pp. 265–294, 1978
1978
Earlier work this paper cites.
B. Natarajan, “Sparse approximate solutions to linear systems,” SIAM Journal on Computing , vol. 24[2], pp. 227–234, 1995
1995
Earlier work this paper cites.
R. Bhatia, Matrix analysis . Springer, 1997
1997
Earlier work this paper cites.
B. Schölkopf, , A. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural Comput. , vol. 10[5], pp. 1299–1319, 1998
1998
Earlier work this paper cites.
A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science , vol. 286[5439], pp. 509–512, 1999
1999
Earlier work this paper cites.
T. Kailath, A. Sayed, and B. Hassibi, Linear estimation . Prentice-Hall, 2000
2000
Earlier work this paper cites.
M. Tipping, “Sparse kernel principal component analysis,” in Conf. on Neural Inform. Process. Syst. , 2000
2000
Earlier work this paper cites.
2002
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex optimization . Cambridge, 2004
2004
Earlier work this paper cites.
C. Bishop, Pattern recognition and machine learning . Springer, 2007
2007
Earlier work this paper cites.
I. Pesenson, “Sampling in paley-wiener spaces on combinatorial graphs,” Trans. of the American Mathematical Society , vol. 360[10], pp. 5603–5627, 2008
2008
Earlier work this paper cites.
A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies,” J. Mach. Learning Research , vol. 9, pp. 235–284, 2008
2008
Earlier work this paper cites.
S. Joshi and S. Boyd, “Sensor selection via convex optimization,” IEEE Trans. Signal Process. , vol. 57[2], pp. 451–462, 2009
2009
Earlier work this paper cites.
A. Marshall, I. Olkin, and B. Arnold, Inequalities: Theory of Majorization and Its Applications . Springer, 2009
2009
Earlier work this paper cites.
Y. Washizawa, “Subset kernel principal component analysis,” in Int. Workshop on Mach. Learning for Signal Process. , 2009
2009
Earlier work this paper cites.
I. Pesenson and M. Pesenson, “Sampling, filtering and sparse approximations on combinatorial graphs,” J. of Fourier Analysis and Applications , vol. 16[6], pp. 921–942, 2010
2010
Earlier work this paper cites.
A. Das and D. Kempe, “Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection,” in Int. Conf. on Mach. Learning , 2011
2011
Cited alongside, same era.
S. Narang and A. Ortega, “Perfect reconstruction two-channel wavelet filter banks for graph structured data,” IEEE Trans. Signal Process. , vol. 60[6], pp. 2786–2799, 2012
2012
Cited alongside, same era.
X. Zhu and M. Rabbat, “Approximating signals supported on graphs,” in Int. Conf. on Acoust., Speech and Signal Process. , 2012, pp. 3921–3924
2012
Cited alongside, same era.
D. 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,” IEEE Signal Process. Mag. , vol. 30[3], pp. 83–98, 2013
2013
Cited alongside, same era.
S. Chen, R. Varma, A. Sandryhaila, and J. Kovačević, “Discrete signal processing on graphs: Sampling theory,” IEEE Trans. Signal Process. , vol. 63[24], pp. 6510–6523, 2015
2015
Later among the works it cites.
B. Mirzasoleiman, A. Badanidiyuru, A. Karbasi, J. Vondrák, and A. Krause, “Lazier than lazy greedy,” in AAAI Conf. on Artificial Intell. , 2015, pp. 1812–1818
2015
Later among the works it cites.
B. Girault, “Stationary graph signals using an isometric graph translation,” in European Signal Process. Conf. , 2015, pp. 1516–1520
2015
Later among the works it cites.
X. Wang, P. Liu, and Y. Gu, “Local-set-based graph signal reconstruction,” IEEE Trans. Signal Process. , vol. 63[9], pp. 2432–2444, 2015
2015
Later among the works it cites.
N. Tremblay, G. Puy, R. Gribonval, and P. Vandergheynst, “Compressive spectral clustering,” in Int. Conf. on Mach. Learning , 2016, pp. 1002––1011
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A. Sandryhaila and J. Moura, “Discrete signal processing on graphs,” IEEE Trans. Signal Process. , vol. 61[7], pp. 1644–1656, 2013
2013
Cited alongside, same era.
S. Narang, A. Gadde, and A. Ortega, “Signal processing techniques for interpolation in graph structured data,” in Int. Conf. on Acoust., Speech and Signal Process. , 2013, pp. 5445–5449
2013
Cited alongside, same era.
G. Sagnol, “Approximation of a maximum-submodular-coverage problem involving spectral functions, with application to experimental designs,” Discrete Appl. Math. , vol. 161[1-2], pp. 258–276, 2013
2013
Cited alongside, same era.
F. Bach, “Learning with submodular functions: A convex optimization perspective,” Foundations and Trends in Machine Learning , vol. 6[2-3], pp. 145–373, 2013
2013
Cited alongside, same era.
D. Feldman, M. Schmidt, and C. Sohler, “Turning big data into tiny data: Constant-size coresets for K-means, PCA and projective clustering,” in ACM-SIAM Symp. on Discrete Algorithms , 2013, pp. 1434–1453
2013
Cited alongside, same era.
J. Arenas-Garcia, K. Petersen, G. Camps-Valls, and L. Hansen, “Kernel multivariate analysis framework for supervised subspace learning: A tutorial on linear and kernel multivariate methods,” IEEE Signal Process. Mag. , vol. 30[4], pp. 16–29, 2013
2013
Cited alongside, same era.
R. Horn and C. Johnson, Matrix analysis . Cambridge University Press, 2013
2013
Cited alongside, same era.
H. Shomorony and A. Avestimehr, “Sampling large data on graphs,” in Global Conf. on Signal and Inform. Process. , 2014, pp. 933–936
2014
Cited alongside, same era.
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,” IEEE Trans. Signal Process. , vol. 64[14], pp. 3775–3789, 2016
2016
Later among the works it cites.
M. Tsitsvero, S. Barbarossa, and P. Di Lorenzo, “Signals on graphs: Uncertainty principle and sampling,” IEEE Trans. Signal Process. , vol. 64[18], pp. 4845–4860, 2016
2016
Later among the works it cites.
S. Chen, R. Varma, A. Singh, and J. Kovačević, “Signal recovery on graphs: Fundamental limits of sampling strategies,” IEEE Trans. Signal Process. , vol. 2[4], pp. 539–554, 2016
2016
Later among the works it cites.
A. Marques, S. Segarra, G. Leus, and A. Ribeiro, “Sampling of graph signals with successive local aggregations,” IEEE Trans. Signal Process. , vol. 64[7], pp. 1832–1843, 2016
2016
Later among the works it cites.
S. Chepuri and G. Leus, “Subsampling for graph power spectrum estimation,” in Sensor Array and Multichannel Signal Process. Workshop , 2016
2016
Later among the works it cites.
F. Gama, A. Marques, G. Mateos, and A. Ribeiro, “Rethinking sketching as sampling: Linear transforms of graph signals,” in Asilomar Conf. on Signals, Syst. and Comput. , 2016
2016
Later among the works it cites.
L. Chamon and A. Ribeiro, “Near-optimality of greedy set selection in the sampling of graph signals,” in Global Conf. on Signal and Inform. Process. , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
P. Di Lorenzo, S. Barbarossa, P. Banelli, and S. Sardellitti, “Adaptive least mean squares estimation of graph signals,” IEEE Trans. Signal Inf. Process. over Netw. , vol. 2[4], pp. 555–568, 2016
2016
Later among the works it cites.
L. Chamon and A. Ribeiro, “Universal bounds for the sampling of graph signals,” in Int. Conf. on Acoust., Speech and Signal Process. , 2017
2017
Closest in time.
N. Perraudin and P. Vandergheynst, “Stationary signal processing on graphs,” IEEE Trans. Signal Process. , vol. 65[13], pp. 3462–3477, 2017
2017
Closest in time.