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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Potluru, V.K. Plis, S.M. Shuang Luan Calhoun, V.D. Hayes, T.P. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science, University of New Mexico, USA (Potluru, V.K.; Shuang Luan; Hayes, T.P.) || Mind Research Network, New Mexico, USA (Plis, S.M.; Calhoun, V.D.) |
| Abstract | Nonnegative Least Squares (NNLS) is a general form for many important problems. We consider a special case of NNLS where the input is nonnegative. It is called Totally Nonnegative Least Squares (TNNLS) in the literature. We show a reduction of TNNLS to a single class Support Vector Machine (SVM), thus relating the sparsity of a TNNLS solution to the sparsity of supports in a SVM. This allows us to apply any SVM solver to the TNNLS problem. We get an order of magnitude improvement in running time by first obtaining a smaller version of our original problem with the same solution using a fast approximate SVM solver. Second, we use an exact NNLS solver to obtain the solution. We present experimental evidence that this approach improves the performance of state-of-the-art NNLS solvers by applying it to both randomly generated problems as well as to real datasets, calculating radiation therapy dosages for cancer patients. |
| Starting Page | 1922 |
| Ending Page | 1929 |
| File Size | 1231978 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033459 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Silicon |
| Content Type | Text |
| Resource Type | Article |
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