Abstract
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 high-dimensional 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 proposes a weighted local discriminant preservation projection algorithm based on an ensemble imbedded mechanism with micro-noise 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.
Highlights
Most of the data generated in real life have high dimensionality
Manifold learning has provided effective ways to improve classification accuracy and generalization ability as well as reduce the complexity and runtime of the model, which helps to solve the curse of dimensionality for high-dimensional data and plays an important role in classification
This paper proposes a weighted local discriminant preservation projection ensemble algorithm with embedded micro-noise
Summary
Most of the data generated in real life have high dimensionality. high-dimensional data often cause the curse of dimensionality in data processing. Some scholars believe that adding noise into the process of training could instruct the model to learn feature representations that are robust to the effect of noise, thereby reducing the risk of overfitting and improving the generalization ability of the model [33]. The problems above are widespread across datasets but are neglected by LPP-related algorithms To solve these problems, this paper proposes a weighted local discriminant preservation projection ensemble algorithm with embedded micro-noise. Micro-noise injection into the process of training could instruct the model to learn feature representations that are robust to the effect of noise, thereby reducing the risk of overfitting and improving the generalization ability of the model.
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