Information retrieval from large data sets via multiple-winners-take-all

Recently, a continuous-time k-winners-take-all (kWTA) network with a single state variable and a hard-limiting activation function and its discrete-time counterpart were developed. These kWTA networks have proven properties of finite-time global convergence and simple architectures. In this paper, the kWTA networks are applied for information retrieval, such as web search. The weights or scores of pages in two real-world data sets are calculated with the PageRank algorithm, based on which experimental results of kWTA networks are provided. The results show that the kWTA networks converge faster as the size of the problem grows, which renders them as a promising approach to large-scale data set information retrieval problems.

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