Abstract：In view of the incremental learning problem of manifold learning algorithm, the adaptive neighborhood incremental PCA-LPP manifold learning algorithm is presented, and incremental learning principle of algorithm is introduced. For incremental sample data, The adjacency and covariance matrix was incremental updated by existing sample, then dimensionality reduction results of incremental sample was estimated by of dimensionality reduction results existing sample and updated matrix, finally, the dimensionality reduction results of incremental and existing sample was updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm was application in processing of gearbox fault signals, the dimensionality reduction results by incremental learning have very small error compared with batch learning, spatial aggregation of incremental sample was basically stable, and fault identification rate was increased.
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