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The Segmental Boosting Algorithm for Time-series Feature Selection

Discriminative feature selection paradigms, e.g., [8, 9] usually consider observation frames in an isolated manner, neglecting temporal dependency in time series. Such temporal relationships provide important information for recognition. We propose Segmental Boosting Algorithm (SBA), which applies feature selection only to the “static segments” of the timeseries. It smoothly fills in the gap between the dynamic nature of the time-series data and the static nature of the feature selection methods.