多信息融合的LPP算法

LPP Algorithm Based on Multi-Information Fusion

针对原始局部保持投影(LPP:Local Preserving Projection)算法难以准确获取非均匀高维数据的局部流形结构且未利用样本类别信息的缺陷,提出一种多信息融合的局部保持投影算法(MIF-LPP:Multi-Information Fusion Local Preserving Projection)。该算法使用改进后的标准欧氏距离获取样本的近邻和互邻信息,降低了样本点分布不均和不同维度数据量纲差异的影响。通过融合样本的类别信息构造权值矩阵,进而获得数据的低维本质流形。最后,分别在CWRU(Case Western Reserve University)数据集和本实验室轴承数据集上验证该算法的有效性。实验结果表明,MIF-LPP算法的特征提取性能明显优于其他算法,并且对邻域值具有鲁棒性。

Aiming at the defect that the original LPP(Local Preserving Projection)algorithm is difficult to accurately obtain the local manifold structure of non-uniform high-dimensional data and can not use the sample category information,a MIF-LPP(Multi-Information Fusion Local Preserving Projection)algorithm is proposed.MIF-LPP algorithm uses the improved standard Euclidean distance to obtain the nearest neighbor information and mutual neighbor information of samples,reducing the impact of uneven distribution of sample points and the difference of data dimensions of a single sample.The weight matrix is constructed by fusing the class information of the samples,and then the low dimensional essential manifold of the data is obtained.The validity of the algorithm is verified on CWRU(Case Western Reserve University)data set and our laboratory bearing data set respectively.The experimental results show that the feature extraction performance of MIF-LPP algorithm is obviously superior to other algorithms,and it is robust to neighborhood values.