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outliers 3

Fixed-point iteration 1

Kalman filtering 1

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detection of outliers 1

dimension reduction 1

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The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Strategic Study of CAE 2009, Volume 11, Issue 2, Pages 82-87

Abstract: style="text-align: justify;">The data dimensionality reduction is the main method that can enhance the outliersThe research of detection of outliers based on manifold learning is proposed after analyzing the advantagesThe algorithm determines weighted values by discretion formula of weighted outliers.Through these weighted values, the experts can identify the outliers easily.Our method gives a new way for the solution of detection of outliers.

Keywords： manifold learning detection of outliers high dimensional data dimensionality reduction outliers

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Outliers detection algorithm based on nonlinear data transformation

Xu Xuesong,Zhang Xu,Song Dongming,Zhang Hong,Liu Fnegyu

Strategic Study of CAE 2008, Volume 10, Issue 9, Pages 74-78

Abstract: p style="text-align: justify;">The data dimension reduction is the main method that can enhance the outliers

Keywords： dimension reduction kernel function principal component outliers

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Robust Maximum Entropy Clustering Algorithm RMEC and Its Outlier Labeling

Deng Zhaohong,Wang Shitong,Wu Xisheng,Hu Dewen

Strategic Study of CAE 2004, Volume 6, Issue 9, Pages 38-45

Abstract: version of the maximum entropy algorithm MEC, is presented to overcome its drawbacks: very sensitive to outlierswith algorithm MEC, the main contributions of algorithm RMEC exist in its much better robustness for outliersand the fact that it can effectively label outliers in the dataset using the obtained weight factorsexperimental results demonstrate its superior performance in enhancing the robustness and labeling outliers

Keywords： entropy clustering robustness outliers ε-insensitive loss function weight factors

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A novel multiple-outlier-robust Kalman filter Research Articles

Yulong HUANG, Mingming BAI, Yonggang ZHANG,heuedu@163.com,mingming.bai@hrbeu.edu.cn,zhangyg@hrbeu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 422-437 doi: 10.1631/FITEE.2000642

Abstract: This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time systems. A new is first proposed to evaluate the similarity between two random vectors from dimension to dimension. Then, the proposed MORKF is derived via maximizing a based cost function. The MORKF guarantees the convergence of iterations in mild conditions, and the boundedness of the approximation errors is analyzed theoretically. The selection strategy for the similarity function and comparisons with existing robust methods are presented. Simulation results show the advantages of the proposed filter.

Keywords： Kalman filtering Multiple statistical similarity measure Multiple outliers Fixed-point iteration