Multivariate locally adaptive density estimation

By Stephan R. Sain. 2002

126 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dad4e7c8e409f49dbc4fe046fef754bd8452f9ba

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Statistics and Probability

Citations in the corpus

Bayesian modeling of dynamic scenes for object detection (2005, methodology): “retical understanding and in terms of practical algorithms, [ 32 ].”
Quick Shift and Kernel Methods for Mode Seeking (2008, methodology): “, λN of the linear dimensions, it might be preferable to use an adapted Parzen window, which retains the same proportions [17].”
Cross‐validation Bandwidth Matrices for Multivariate Kernel Density Estimation (2005, background): “While some progress has been made, (e.g. Sain, 2002), this remains a challenging research direction.”
Maximum likelihood estimation of a multi‐dimensional log‐concave density (2008, methodology): “Log-concave density estimation 13 (vi) Sain’s method (Sain, 2002; Scott and Sain, 2004).”
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011, unknown): “If they were forced to be χn query-adaptive, i.e. κ1D λi , the resulting function would not integrated to 1 and would not meet the definition of a PDF (Sain, 1994).”
Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, background): “It is effective even when the sample space is multidimensional [Sain, 2002].”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, unknown): “Adaptive-width kernels can lead to improvement over fixed-width kernels as adaptive kernels are more flexible and are better able to model complex distributions (Herrmann, 2000; Sain, 2002; Wand and Jones, 1994, 1995; Worton, 1989).”
Kernel bandwidth optimization in spike rate estimation (2009, result): “However, in contrast to the progress in methods that vary bandwidths at sample points only (Abramson 1982; Breiman et al. 1977; Sain and Scott 1996; Sain 2002; Brewer 2004), the local optimization of band-width at every instant turned out to be difﬁcult because of its excessive freedom (Scott 1992;…”