| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1708247 | Applied Mathematics Letters | 2013 | 5 Pages |
Abstract
We formalize an algorithm for solving the L1L1-norm best-fit hyperplane problem derived using first principles and geometric insights about L1L1 projection and L1L1 regression. The procedure follows from a new proof of global optimality and relies on the solution of a small number of linear programs. The procedure is implemented for validation and testing. This analysis of the L1L1-norm best-fit hyperplane problem makes the procedure accessible to applications in areas such as location theory, computer vision, and multivariate statistics.
Related Topics
Physical Sciences and Engineering
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Authors
J.P. Brooks, J.H. Dulá,