EagleEye detects localized over- and under-densities in multivariate data by binomial testing of kNN membership sequences between two samples, followed by deterministic consolidation into anomaly sets.
Automatic topography of high-dimensional data sets by non-parametric density peak clustering
2 Pith papers cite this work. Polarity classification is still indexing.
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BMTI estimates log-density via integration of neighbor differences on data manifolds using maximum-likelihood weighting, without binning or explicit coordinates.
citing papers explorer
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Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics
EagleEye detects localized over- and under-densities in multivariate data by binomial testing of kNN membership sequences between two samples, followed by deterministic consolidation into anomaly sets.
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Density Estimation via Binless Multidimensional Integration
BMTI estimates log-density via integration of neighbor differences on data manifolds using maximum-likelihood weighting, without binning or explicit coordinates.