For the resulting quantizing density estimator (QDE) we present a general method for parameter estimation and model selection. We show how projection sets which ...
We suggest a nonparametric framework for unsupervised learning of projection models in terms of density estimation on quantized sample spaces.
Well known approaches include kernel estimators, projection pursuit, tree- structured algorithms such as CART, and \partition-based" density estimators, which.
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The influence of rounding errors on some nonparametric estimators of a density and its derivatives. SIAM J. Appl. Math., 42 (1982), pp. 390-399.
Jan 3, 2001 · We show how projection sets which correspond to traditional unsupervised methods like vector quantization or PCA appear in the new framework.
A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization.
We propose an approach to source adaptivity in ICA based on quantizing density estimators (QDE). These estimators allow to realize source adaptivity in an ...
We consider aspects of estimating conditional and unconditional densities in conjunction with Bayes-risk weighted vector quantization for joint compression and ...
Jul 4, 2023 · Abstract: We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel ...
In this article we investigate the problem of density estimation from a data set which has undergone the quantization process. In particular we consider the ...