camel-package:
camel: Calibrated Machine Learning
Description
The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
Details
|
Package: |
| camel |
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Type: |
| Package |
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Version: |
| 0.2.0 |
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Date: |
| 2013-09-09 |
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License: |
| GPL-2 |
References
1. A. Belloni, V. Chernozhukov, and L. Wang. Pivotal recovery of sparse signals via conic programming. Biometrika, 2012.
2. L. Wang. L1 penalized LAD estimator for high dimensional linear regression. Journal of Multivariate Analysis, 2013.
3. E. Candes and T. Tao. The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics, 2007.
4. T. Cai, W. Liu, and X. Luo. A constrained $\ell_1$ minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association, 2011.
5. H. Liu and L. Wang. TIGER: A tuning-insensitive approach for optimally estimating large undirected graphs. Technical Report, 2012.
6. L. Han, L. Wang, and T. Zhao. Multivariate Regression with Calibration. http://arxiv.org/abs/1305.2238, 2013.
7. T. Zhao and H. Liu, Sparse Precision Matrix Estimation with Calibration. Advances in Neural Information Processing systems, 2013.
8. A. Beck and M. Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 2009.
9. Y. Nesterov. Smooth minimization of non-smooth functions. Mathematical Programming, 2005.