Data in all Signal Processing (SP) applications is being generated super-exponentially, and at an ever increasing rate. A meaningful way to pre-process it so as to achieve feasible computation is by Partitioning the data [5]. Indeed, the task of partitioning is one of the most difficult problems in computing, and it has extensive applications in solving real-life problems, especially when the amount of SP data (i.e., images, voices, speakers, libraries etc.) to be processed is prohibitively large. The problem is known to be NP-hard. The benchmark solution for this for the Equi-partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in numerous application domains. While the OMA is a fixed structure machine, it does not incorporate the Pursuit concept that has, recently, significantly enhanced the field of LA. In this paper, we pioneer the incorporation of the Pursuit concept into the OMA. We do this by a non-intuitive paradigm, namely that of removing (or discarding) from the query stream, queries that could be counter-productive. This can be perceived as a filtering agent triggered by a pursuit-based module. The resulting machine, referred to as the Pursuit OMA (POMA), has been rigorously tested in all the standard benchmark environments. Indeed, in certain extreme environments it is almost ten times faster than the original OMA. The application of the POMA to all signal processing applications is extremely promising.

Additional Metadata
Keywords Learning Automata, Object Migration Automaton, Object Partitioning, Partitioning-based Learning
Persistent URL dx.doi.org/10.1109/MLSP.2017.8168149
Conference 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
Citation
Shirvani, A. (Abdolreza), & Oommen, J. (2017). Partitioning in signal processing using the object migration automaton and the pursuit paradigm. In IEEE International Workshop on Machine Learning for Signal Processing, MLSP (pp. 1–7). doi:10.1109/MLSP.2017.8168149