Entropy Measures for Data Analysis: Theory, Algorithms and ApplicationsKarsten Keller Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given. |
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algorithm analysis AOA1 AOA2 ApEn application approach auditory object-specific attention authors average belief entropy calculated changes channels classes classification CmpMSE compared comparison complexity components Comput considered corresponding CrossRef dataset defined detection determined distance distribution dynamical EEG signals effect elements ENSO entropy measures equation estimation evaluate example experimental experiments fault Figure follows frequency function fuzzy given identification increasing indices learning length means method multivariate noise obtained optimization ordinal patterns parameters patterns performance period permutation Phys points postural presented probability problem properties proposed PubMed random range records representation represents respectively Rest SampEn samples selection Shannon shown shows significant similar soft stability statistical Step synchronization Table theory transfer transformation uncertainty measure updates values variables vector walking weight