Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/85034
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DC Field | Value | Language |
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dc.contributor.author | Shen, F. | - |
dc.contributor.author | Shen, C. | - |
dc.contributor.author | Hill, R. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.contributor.author | Tang, Z. | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Computational Statistics and Data Analysis, 2014; 77:25-37 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.issn | 1872-7352 | - |
dc.identifier.uri | http://hdl.handle.net/2440/85034 | - |
dc.description.abstract | Minimization of the L∞ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression. However, current techniques for solving the problem at the heart of L∞ norm minimization are slow, and therefore cannot be scaled to large problems. A new method for the minimization of the L∞ norm is presented here, which provides a speedup of multiple orders of magnitude for data with high dimension. This method, termed Fast L∞ Minimization, allows robust regression to be applied to a class of problems which was previously inaccessible. It is shown how the L∞ norm minimization problem can be broken up into smaller sub-problems, which can then be solved extremely efficiently. Experimental results demonstrate the radical reduction in computation time, along with robustness against large numbers of outliers in a few model-fitting problems. © 2014 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | Fumin Shen, Chunhua Shen, Rhys Hill, Anton van den Hengel, Zhenmin Tang | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Science | - |
dc.rights | © 2014 Elsevier B.V. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.csda.2014.02.018 | - |
dc.subject | Least-squares regression; outlier removal; robust regression; face recognition | - |
dc.title | Fast approximate L∞ minimization: speeding up robust regression | - |
dc.title.alternative | Fast approximate L infinity minimization: speeding up robust regression | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.csda.2014.02.018 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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RA_hdl_85034.pdf Restricted Access | Restricted Access | 725.73 kB | Adobe PDF | View/Open |
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