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A Fast Similarity Calculation Method Based on Cotangent Similarity and BP Neural Network
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College of Electronics and Information Engineering， Tongji University， Shanghai 201804， China

Clc Number:

TP311.1

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    Abstract:

    Similarity measurement is of great significance in big data related applications. However， the traditional cosine similarity traversal calculation method has a poor accuracy and timeliness， which cannot provide an effective basis for the quality assessment of massive high-dimensional data. To improve the accuracy of similarity calculation， two types of cotangent similarity formulas with cotangent trigonometric function and data dimensional differences was constructed. Besides， a back-propagation（BP） neural network model approximating the similarity mapping relationship of datasets was established to reduce the time complexity. The experimental results demonstrate that the improved fast similarity calculation method has a good accuracy and timeliness. Moreover， it has a more significant performance improvement when applied to large-scale datasets.

    Table 5
    Fig.1 Schematic diagram of relationship between two-dimensional vectors
    Fig.2 Flowchart of fast similarity calculation based on cotangent similarity and BP neural network
    Fig.3 Pseudocode of fast similarity calculation based on cotangent similarity and BP neural network
    Fig.4 Similarity calculation error based on neural network and traversal calculation(CWRU subdatasets)
    Fig.5 Comparison of running time of similarity calculation based on different calculation formulas
    Fig.6 Comparison of running time of similarity calculation based on different calculation methods
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QIAO Fei, GUAN Liuen, WANGE Qiaoling. A Fast Similarity Calculation Method Based on Cotangent Similarity and BP Neural Network[J].同济大学学报(自然科学版),2021,49(1):153~162

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  • Received:August 27,2020
  • Online: February 26,2021
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