2012/02/24 by Mike Gashler, Tony Martinez · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Face and Expression Recognition #Neural Networks and Applications
paper · pdf · doi:10.1080/09540091.2012.664122
openalex publication_date 2012/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Many manifold learning algorithms utilise graphs of local neighbourhoods to estimate manifold topology. When neighbourhood connections short-circuit between geodesically distant regions of the manifold, poor results are obtained due to the compromises that the manifold learner must make to satisfy the erroneous criteria. Also, existing manifold learning algorithms have difficulty in unfolding manifolds with toroidal intrinsic variables without introducing significant distortions to local neighbourhoods. An algorithm called CycleCut is presented, which prepares data for manifold learning by removing short-circuit connections and by severing toroidal connections in a manifold.