计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 323-328.doi: 10.11896/jsjkx.191000012
• 计算机网络 • 上一篇
张俊, 王杨, 李坤豪, 李昌, 赵传信
ZHANG Jun, WANG Yang, LI Kun-hao, LI Chang, ZHAO Chuan-xin
摘要: 无线体域网多传感器由于采集的数据量大、数据类型冗杂, 难以有效进行多维度数据的融合。虽然传统流形及分解类型的数据融合方法(Isomap, MDS, PCA等)具备使距离较小点产生合理排斥梯度的能力和受异常影响较小的优点, 但是针对无线多传感器体域网的数据降维效果并不理想。对此, 提出了一种基于流形学习的T分布式随机邻域嵌入(T-SNE)算法对多传感器体域网数据进行融合。T-SNE算法首先将高维数据点与其对应的低维数据点间的欧氏距离转换为条件概率矩阵, 然后对处理好的低维概率集合进行有限次迭代, 最后更新低维概率矩阵, 使距离较小点间产生合理的排斥梯度, 从而构建了多维度体域网数据融合模型。实验结果表明, 在特定的体域网数据集下, T-SNE算法的精度为Isomap的1.11倍, MDS的1.33倍, PCA的1.21倍, 具有较好的数据降维效果。
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