Disturbance rejection with compensation on features

In pattern recognition tasks, the information from system input is modeled through a series of nonlinear operations, which include but not limited to feature extraction, regression, and classification. Both theoretically and practically, these operations are inevitably subject to internal modeling error and external disturbance, resulting at a performance challenge. Those state-of-the-art methods, e.g. Convolutional Neural Network and Transformer, still display significant instabilities and failures under practical applications, so comes a lack of generalization. Consequently, the more robust pattern recognition methods and related theories still merit a further study. This paper firstly reviews those state-of-the-art technologies in the field. The bottleneck of performances in those latest researches is associated with a lack of disturbance estimation and corresponding compensation. Therefore, the implications of disturbance rejection in pattern recognition field are further discussed from a control point of view. Then, the open problems are summarized. Ultimately, a discussion of the potential solutions, which is related to the application of compensation on features, is given to highlight the future study. Through the systematic review in this paper, the disturbance rejection in pattern recognition is developed into a control problem. Hopefully, more effective control technologies for the compensation on features can be used to improve the robustness of pattern recognition theoretically and practically.

Automated summary

This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.