GLOBE: A novel pruning-based sparse modeling with application to time series data representation

In the past years, Sparse Modeling (SM) has been used in a variety of applications, such as data representation. In this type of data modeling, the data is divided into small segments, and each one is processed independently using SM techniques to find the best few coefficients that capture the most important features of the segment. This paper proposes a backward stage for SM to process all segments at the same time. The main idea is to select significant coefficients that capture the global features of the data, not just the features of the small segments. To evaluate the effectiveness of the proposed algorithm, extensive experiments were conducted on different time series data. The results showed that the proposed algorithm works better with time series data that has localized features, such as speech data. The study also compared the proposed algorithm to other recent backward techniques and found that it outperforms the others significantly, especially when using learned bases.