Quantizing for minimum distortion

By J. Max. 1960

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Citations in the corpus

Price of Transparency in Strategic Machine Learning (2016, methodology): “We use standard game theoretic notations for the related results throughout this paper (cf. [7]).”
Deterministic annealing for clustering, compression, classification, regression, and related optimization problems (1998, methodology): “In the communications or information-theory literature, an early clustering method was suggested for scalar quantization, which is known as the Lloyd algorithm [60] or the Max quantizer [65].”
Empirical quantizer design in the presence of source noise or channel noise (1997, methodology): “On the practical side, the Lloyd‐Max [1], [ 2 ] algorithm provides an efficient iterative method of designing locally optimal quantizers from known source statistics or from training samples.”
Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data (2005, methodology): “In Max (1960), optimal discretization points were computed to transform a continuous variable into a categorical one, in which the number of categories can vary from 2 to 36.”
Introduction to Digital Speech Processing (2007, methodology): “The fundamentals of optimum quantization were established by Lloyd [71] and Max [79].”
Efficient scalar quantization of exponential and Laplacian random variables (1996, background): “One necessary condition for optimality is that the optimal value of each reconstruction offset 6: must minimize the distortion contribution of its associated step interval [ 6 ]”
The Effectiveness of Lloyd-Type Methods for the k-Means Problem (2006, methodology): “This and similar iterative descent methods soon became the dominant approaches to the problem [35, 33, 12, 31] (see also [19, 20, 24] and the references therein); they remain so today, and are still being improved [1, 40, 42, 28].”
Uplink Performance of Wideband Massive MIMO With One-Bit ADCs (2016, background): “That is the reason the limit coincides with the mean-squared error of one-bit quantization in [29] and what is called the distortion factor of one-bit ADCs in [30].”
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016, background): “TABLE I THE OPTIMUM UNIFORM QUANTIZER FOR A GAUSSIAN UNIT-VARIANCE INPUT SIGNAL [29]”
RolX: structural role extraction & mining in large graphs (2012, methodology): “Because the model can contain high-precision floating point values, we combine Lloyd-Max quantization [22, 21] with Huffman codes [17] to increase compression.”