An approach to the mean shift outlier model by Tikhonov regularization and conic programming

Date

2014-01-01

Author

TAYLAN, PAKİZE
Yerlikaya-Oezkurt, Fatma
Weber, Gerhard Wilhelm

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically conic quadratic programming, permitting the use of interior point methods. We present numerical examples, which reveal very good results, and we conclude with an outlook to future studies.

Subject Keywords

Theoretical Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/56787

Journal

INTELLIGENT DATA ANALYSIS

DOI

https://doi.org/10.3233/ida-130629

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

P. TAYLAN, F. Yerlikaya-Oezkurt, and G. W. Weber, “An approach to the mean shift outlier model by Tikhonov regularization and conic programming,” INTELLIGENT DATA ANALYSIS, pp. 79–94, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56787.