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Neurodynamics-based Parallel Data Selection in the Era of Big Data
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| In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed. |
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Date: Mar 1 (Thu), 2018
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Time: 12:30pm to 2:00pm
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| Speaker: |
Chair Professor,
Department of Computer Science,
City University of Hong Kong
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| Venue: |
Room 14-221, 14/F, Lau Ming Wai Academic Building, City University of Hong Kong |
| Language: |
English |
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