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

Metadata

Show full item record

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

Item Usage Stats

175
views

0
downloads

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

Suggestions

OpenMETU
Core

A statistical approach to sparse multi-scale phase-based stereo Ulusoy, İlkay (Elsevier BV, 2007-09-01) In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
On output independence and complementariness in rank-based multiple classifier decision systems Saranlı, Afşar (Elsevier BV, 2001-12-01) This study presents a theoretical analysis of output independence and complementariness between classifiers in a rank-based multiple classifier decision system in the context of the partitioned observation space theory. To enable such an analysis, an information theoretic interpretation of a rank-based multiple classifier system is developed and basic concepts from information theory are applied to develop measures for output independence and complementariness. It is shown that output independence of classi...
Continuous dimensionality characterization of image structures Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04) Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Data mining in deductive databases using query flocks Toroslu, İsmail Hakkı (Elsevier BV, 2005-04-01) Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deduct...
Key protected classification for collaborative learning Sariyildiz, Mert Bulent; Cinbiş, Ramazan Gökberk; Ayday, Erman (Elsevier BV, 2020-08-01) © 2020Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN...

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.