Robust regression and outlier detection
Robust regression and outlier detection
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Enhancing Density-Based Data Reduction Using Entropy
Neural Computation
Multiresolution instance-based learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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In this paper we present a non parametric density-based data reduction technique designed to be used in robust parameter estimation problems. Existing approaches are focused on reducing the amount of data preserving the density function. In our case the reduction is oriented to automatically remove the samples that are considered non interesting while taking into account those that are meaningful, those that have a high density associated. We use this filtering process to simplify the data sets in order to improve the performance of robust parameter estimators. We show its results when used along an existing estimator on synthetic and real LADAR data.