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An Equivalence between the Lasso and Support Vector Machines
| Content Provider | Scilit |
|---|---|
| Author | Jaggi, Martin |
| Copyright Year | 2014 |
| Description | As a consequence, many existing optimization algorithms for both SVMs and Lasso can also be applied to the respective other problem instances. Also, the equivalence allows for many known theoretical insights for SVM and Lasso to be translated between the two settings. One such implication gives a simple kernelized version of the Lasso, analogous to the kernels used in the SVM setting. Another consequence is that the sparsity of a Lasso solution is equal to the number of support vectors for the corresponding SVM instance, and that one can use screening rules to prune the set of support vectors. Furthermore, we can relate sublinear time algorithms for the two problems, and give a new such algorithm variant for the Lasso. We also study the regularization paths for both methods. Book Name: Regularization, Optimization, Kernels, and Support Vector Machines |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2013-0-27442-8&isbn=9780429076121&doi=10.1201/b17558-4&format=pdf |
| Ending Page | 44 |
| Page Count | 26 |
| Starting Page | 19 |
| DOI | 10.1201/b17558-4 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2014-10-23 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Regularization, Optimization, Kernels, and Support Vector Machines Artificial Intelligence Optimization Svm Theoretical Lasso Paths Kernels Analogous Support Vectors Give |
| Content Type | Text |
| Resource Type | Chapter |
