Statistical Learning
AlgorithmicStabilityandGeneralizationofan UnsupervisedFeatureSelectionAlgorithm
Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper,we propose an innovativeunsupervised feature selection algorithm attaining this stability with provable guarantees.
2 Problemsetupandfulldataresults
However, if the negative instances are subsampled to the same level of the positive cases, thereisinformationloss.
Learning outside the Black-Box: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models - but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models.