@conference {27285,
	title = {Uncertainty principles for signals defined on graphs: Bounds and characterizations},
	booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing},
	year = {2012},
	note = {See, also, the extended journal version:

A. Agaskar and Y. M. Lu, A Spectral Graph Uncertainty Principle, IEEE Transactions on Information Theory, to appear.},
	month = {Mar.},
	address = {Kyoto},
	abstract = {The classical uncertainty principle provides a fundamental tradeoff in the localization of a signal in the time and frequency domains. In this paper we describe a similar tradeoff for signals defined on graphs. We describe the notions of "spread" in the graph and spectral domains, using the eigenvectors of the graph Laplacian as a surrogate Fourier basis. We then describe how to find signals that, among all signals with the same spectral spread, have the smallest graph spread about a given vertex. For every possible spectral spread, the desired signal is the solution to an eigenvalue problem. Since localization in graph and spectral domains is a desirable property of the elements of wavelet frames on graphs, we compare the performance of some existing wavelet transforms to the obtained bound.
},
	author = {Agaskar, A. and Lu, Y.M.}
}
