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Regularization and variable selection via the elastic net
It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Least angle regression
A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
Regularization Paths for Generalized Linear Models via Coordinate Descent.
In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
The Elements of Statistical Learning
A working guide to boosted regression trees.
This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model.
Sparse inverse covariance estimation with the graphical lasso.
Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
- T. Sørlie, C. Perou, A. Børresen-Dale
- BiologyProceedings of the National Academy of Sciences…
- 11 September 2001
Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
Sparse Principal Component Analysis
This work introduces a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings and shows that PCA can be formulated as a regression-type optimization problem.
An Introduction to Statistical Learning
- Gareth M. James, D. Witten, T. Hastie, R. Tibshirani
- Computer ScienceSpringer Texts in Statistics
An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.