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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chen, Jianhui Liu, Jun Ye, Jieping |
| Abstract | Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the $l_{1}-norm$ regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving large-scale sparse logistic regression. Specifically, we formulate the problem as the $l_{1}-ball$ constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems. |
| Starting Page | 547 |
| Ending Page | 556 |
| Page Count | 10 |
| File Format | PDF MP4 |
| ISBN | 9781605584959 |
| DOI | 10.1145/1557019.1557082 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-06-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Logistic regression l1-ball constraint Nesterov's method Adaptive line search Sparse learning |
| Content Type | Audio Text |
| Resource Type | Article |
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