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电子学报  2012 

基于秩2更新的多维数据流典型相关跟踪算法

DOI: 10.3969/j.issn.0372-2112.2012.09.011, PP. 1765-1774

Keywords: 多维数据流,典型相关分析,秩2更新,快速跟踪,特征子空间

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Abstract:

现存的多维数据流典型相关分析(CanonicalCorrelationAnalysis,简称CCA)算法主要是基于近似技术的求解方法,本质上并不是持续更新的精确算法.为了能在时变的环境中持续、快速而精确地跟踪数据流之间的相关性,本文提出一种多维数据流典型相关跟踪算法TCCA.该算法基于秩2更新理论,通过并行方式持续更新样本协方差矩阵的特征子空间,进而实现多维数据流典型相关的快速跟踪.理论分析及仿真实验结果表明,TCCA具有较好的稳定性、较高的计算效率和精度,可以作为基本工具应用于数据流相关性检测、特征融合、数据降维等数据流挖掘领域.

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