Abstract: The traditional LE(Laplacian Eigenmaps) algorithm uses Euclidean distance to measure the position relationship between sample points, which is only applicable to linear data sets. However, the data in practical engineering often show strong non-linearity, which makes the final embedding results difficult to reflect the essential characteristics of the original data. An algorithm for D-LE(Double metric constraint Laplace Eigenmaps) based on Double metric constraint is proposed. The algorithm uses cosine similarity to evaluate the similarity between samples, and combines the measurement relations between samples and between samples and local manifolds to build dimensionality reduction model. Experiments on three bearing datasets show that this method can significantly improve the dimensionality reduction effect for processing nonlinear datasets.

Key words: laplace characteristic map, cosine similarity, double measure constraint, bearing fault diagnosis