Melanie F. Pradier

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We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer’s output, Gish and Pierce showed that uniform quantizing yields the smallest output entropy in the limit D → 0, resulting in a rate penalty of 0.255 bits/sample above the Shannon Lower(More)
This paper introduces a general Bayesian nonparametric latent feature model suitable to perform automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First, it accounts for heterogeneous data while(More)
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