BMTI estimates log-density via integration of neighbor differences on data manifolds using maximum-likelihood weighting, without binning or explicit coordinates.
A density-based algorithm for discovering clusters in large spatial databases with noise
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
An enhanced localized kernel method extends the Signal Separation Operator to separate linear chirps under crossovers, very low SNR, and discontinuities, with noise analysis and tests on seven simulated signals.
citing papers explorer
-
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.
-
Localized kernel method for separation of linear chirps
An enhanced localized kernel method extends the Signal Separation Operator to separate linear chirps under crossovers, very low SNR, and discontinuities, with noise analysis and tests on seven simulated signals.