2008
DOI: 10.1109/tip.2008.922429
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Adaptive Local Linear Regression With Application to Printer Color Management

Abstract: Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the… Show more

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Cited by 36 publications
(14 citation statements)
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“…Parameter estimation stage of the TGMRF descriptors suffers from producing estimates that are biased and over-smoothed when the GMRF model do not capture the underlying data generating process (Gupta et al, 2008). This reduce the texture discriminative power of TGMRF features.…”
Section: Local Parameter Histogram (Lph) Descriptorsmentioning
confidence: 99%
“…Parameter estimation stage of the TGMRF descriptors suffers from producing estimates that are biased and over-smoothed when the GMRF model do not capture the underlying data generating process (Gupta et al, 2008). This reduce the texture discriminative power of TGMRF features.…”
Section: Local Parameter Histogram (Lph) Descriptorsmentioning
confidence: 99%
“…Another approach is to build a local model as a Taylor series of the global model expanded in the vicinity of the query (Su et al, 2012). Gupta et al (2008) proved that the variance of estimates of LLR is bounded by the variance of measurement noise if a query is in the convex hull enclosing its neighborhood.…”
Section: Just-in-time Modelingmentioning
confidence: 99%
“…Local machine learning has shown a comparative advantage in many machine learning tasks [1517]. In some situations, the size of local region of target data imposes a significant effect on prediction accuracy of model [17].…”
Section: Local Regression Transfer Learningmentioning
confidence: 99%