基于低秩因子最优估计的特征级数据融合

FEATURE LEVEL DATA FUSION BASED ON OPTIMAL ESTIMATION OF LOW RANK FACTORS

为了提升信息融合系统对噪声的鲁棒性以及计算效率,并且考虑特征之间的关联性,提出一种基于低秩因子最优估计的特征级数据融合方法。通过优化潜在特征向量与从线性组合估计值中提取的余弦相似性度量,找到潜在信号特征空间的估计值。然后利用随机矩阵理论进行特征及数据融合,用于求解具有不同噪声水平的约束数据驱动优化问题。在两个数据集上的实验结果表明,该方法效果显著。

In order to improve the robustness to noise and computational efficiency,and considering the correlation between features,a feature level data fusion method based on optimal estimation of low rank factor is proposed.By optimizing the cosine similarity measure between the potential feature vector and the vector extracted from the linear combination estimation,the estimation of the potential signal feature space was obtained.The stochastic matrix theory was used for feature and data fusion to solve constrained data-driven optimization problems with different noise levels.Experimental results on two datasets show that the effect of the proposed method is remarkable.