Abstract
Recent non-linear feature selection approaches employing greedy optimisation
of Centred Kernel Target Alignment(KTA) exhibit strong results in terms of
generalisation accuracy and sparsity. However, they are computationally
prohibitive for large datasets. We propose randSel, a randomised feature
selection algorithm, with attractive scaling properties. Our theoretical
analysis of randSel provides strong probabilistic guarantees for correct
identification of relevant features. RandSel's characteristics make it an ideal
candidate for identifying informative learned representations. We've conducted
experimentation to establish the performance of this approach, and present
encouraging results, including a 3rd position result in the recent ICML black
box learning challenge as well as competitive results for signal peptide
prediction, an important problem in bioinformatics.
Users
Please
log in to take part in the discussion (add own reviews or comments).