Abstract:

For the problem that LLE(Local Linear Embedding) fails to adequately preserve the structure between

neighborhoods in high-dimensional data, a new local linear embedding algorithm is proposed for fused

neighborhood distribution properties. The algorithm calculates the neighborhood distribution of each sample data,

then calculates the respective nearest neighborhood distribution difference of the KL ( Kullback-Leibler)

divergence measure between the different neighborhood point and its central sample, and finally optimizes the

reconstructed weight coefficient to obtain more accurate low-dimensional motor data. The effectiveness of the

algorithm is verified by three evaluations of visualization, Fisher measurement and identification accuracy.

Key words: local linear embedding, neighborhood distribution, dimension reduction algorithm, motor bearing