GMMs similarity measure based on LPP-like projection of the parameter space
作者：
Highlights：
• We propose the novel more efficient similarity measure between GMMs.
• It is done by projecting GMMs from high dimensional to a lower dimensional space.
• GMMs distance is reduced to the distance between lower dimensional euclidian vectors.
• Greater discriminativity and lower computational cost is obtained.
• We confirm our results on artificial and real experimental data.
摘要
•We propose the novel more efficient similarity measure between GMMs.•It is done by projecting GMMs from high dimensional to a lower dimensional space.•GMMs distance is reduced to the distance between lower dimensional euclidian vectors.•Greater discriminativity and lower computational cost is obtained.•We confirm our results on artificial and real experimental data.
论文关键词：Gaussian mixture model,Similarity measures,Dimensionality reduction,KL-divergence
论文评审过程：Received 12 January 2016, Revised 31 August 2016, Accepted 8 September 2016, Available online 9 September 2016, Version of Record 14 September 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.014
