Abstract: The local linear embedding algorithm uses Euclidean distance to select neighborhood points,which usually loses the nonlinear features of the dataset itself,resulting in incorrect selection of neighborhood points,and only using Euclidean distance to construct weights leads to insufficient information mining.To address the above issues,a local linear embedding algorithm based on probability model and information entropy(PIE-LLE algorithm) is proposed.Firstly,in order to make the selection of neighborhood points more reasonable,from the perspective of the probability distribution of the dataset,the probability distribution of the sample points and their neighborhoods is considered,and a neighborhood set that conforms to the local distribution is constructed for the sample points.Secondly,in order to fully extract the local structural information of the samples,in the weight construction stage,the probability of the neighborhood to which the samples belong and the information entropy of each sample are calculated separately,and the two information are fused to reconstruct the low dimensional samples.Finally,experiments on two bearing fault datasets show that the highest accuracy of fault identification reaches 100%,higher than that of other comparative algorithms.Within the range of 5~15 neighborhood points,the PIE-LLE algorithm exhibits good low dimensional visualization performance.In the parameter sensitivity experiment,the proposed algorithm can maintain a relatively large Fisher index,effectively improving the classification accuracy and stability of the algorithm.

Key words: Local linear embedding algorithm, Probability model, Information entropy, Feature extraction, Fault diagnosis