High-dimensional data often cause the "curse of dimensionality" in data processing. Dimensionality reduction can effectively solve the curse of dimensionality and has been widely used in highdimensional data processing. However, the existing dimensionality reduction algorithms neglect the effect of noise injection, failing to account for the datasets of large variance within classes and not effectively considering the stability of dimensionality reduction. To solve the problems, this paper

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... ses a weighted local discriminant preservation projection algorithm based on an ensemble imbedded mechanism with micronoise injection (n_w_LPPD). The proposed algorithm aims to overcome the problem of large variance within classes and introduces an ensemble projection matrix via Bayesian fusion mechanism with micro-noise to enhance the antijamming capability of the model. Ten public datasets were used to verify the proposed algorithm. The experimental results demonstrated that the proposed algorithm is significantly effective, especially for the case of small sample datasets with high intraclass variance. The classification accuracy is improved by at least 10% compared to the case without dimensionality reduction. Even compared with some representative dimensionality reduction algorithms, the proposed n_w_LPPD has significantly superior classification performance. INDEX TERMS High-dimensional data, curse of dimensionality, ensemble projection matrix, Bayesian fusion, manifold learning, dimensionality reduction, small sample datasets. 143814 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 7, 2019