Density-preserving quantization with application to graph downsampling


Morteza Alamgir, Gábor Lugosi, Ulrike Luxburg ;
Proceedings of The 27th Conference on Learning Theory, PMLR 35:543-559, 2014.


We consider the problem of vector quantization of i.i.d. samples drawn from a density p on \mathbbR^d. It is desirable that the representatives selected by the quantization algorithm have the same distribution p as the original sample points. However, quantization algorithms based on Euclidean distance, such as k-means, do not have this property. We provide a solution to this problem that takes the unweighted k-nearest neighbor graph on the sample as input. In particular, it does not need to have access to the data points themselves. Our solution generates quantization centers that are “evenly spaced". We exploit this property to downsample geometric graphs and show that our method produces sparse downsampled graphs. Our algorithm is easy to implement, and we provide theoretical guarantees on the performance of the proposed algorithm.

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