DPC-SCREENING.GITHUB.IO

DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set

DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set. This package implements the highly effective screening rules — developed under the DPC framework — for many popular sparse learning models, like Lasso, non-negative Lasso, group Lasso, sparse-group Lasso etc. Highly effective in reducing the data size by discarding the irrelevant variables. Highly effective in speeding up any. Existing algorithms for solving the sparse learning models. DPC package 2.1.1.

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DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set | dpc-screening.github.io Reviews

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DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set. This package implements the highly effective screening rules — developed under the DPC framework — for many popular sparse learning models, like Lasso, non-negative Lasso, group Lasso, sparse-group Lasso etc. Highly effective in reducing the data size by discarding the irrelevant variables. Highly effective in speeding up any. Existing algorithms for solving the sparse learning models. DPC package 2.1.1.

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2 download

3 others

4 references

5 revision history

6 features including

7 software download

8 source

9 coupons

10 reviews

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DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set | dpc-screening.github.io Reviews

https://dpc-screening.github.io

DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set. This package implements the highly effective screening rules — developed under the DPC framework — for many popular sparse learning models, like Lasso, non-negative Lasso, group Lasso, sparse-group Lasso etc. Highly effective in reducing the data size by discarding the irrelevant variables. Highly effective in speeding up any. Existing algorithms for solving the sparse learning models. DPC package 2.1.1.

INTERNAL PAGES

dpc-screening.github.io

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DPC - References

http://dpc-screening.github.io/references.html

Lasso Screening Rules via Dual Polytope Projection. Improved version of the one accepted by NIPS 2013). Jie Wang, Peter Wonka, and Jieping Ye. Journal of Machine Learning Research, to appear. Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets. Jie Wang and Jieping Ye. A Safe Screening Rule for Sparse Logistic Regression. Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, and Jieping Ye. Scaling SVM and Least Absolute Deviations via Exact Data Reduction. NIPS 2013. Spotlight.

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DPC - Revision History

http://dpc-screening.github.io/revision.html

DPC - Revision History. DPC package 2.1.1. TLFre rule for Sparse-Group Lasso is added. DPC rule for non-negative Lasso is added. EDPP rule for group Lasso is added. The preliminary version of DPC package is accomplished. Page generated 2014-12-05 13:53:45 US Mountain Standard Time, by jemdoc.

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DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set

http://dpc-screening.github.io/index.html

DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set. This package implements the highly effective screening rules — developed under the DPC framework — for many popular sparse learning models, like Lasso, non-negative Lasso, group Lasso, sparse-group Lasso etc. Highly effective in reducing the data size by discarding the irrelevant variables. Highly effective in speeding up any. Existing algorithms for solving the sparse learning models. DPC package 2.1.1.

4

DPC: Download

http://dpc-screening.github.io/download.html

This package can be downloaded here: [ Download. Page generated 2014-12-05 13:53:20 US Mountain Standard Time, by jemdoc.

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DPC: Introduction

http://dpc-screening.github.io/introduction.html

Here is a brief introduction of the package. The idea of “screening” has been shown very promising in solving Lasso for large-scale problems. Essentially, screening for Lasso aims to quickly identify the “inactive features” that have 0 components in the solution and then remove them from the optimization. Therefore, we can work on a reduced feature matrix to solve the Lasso problem, which may lead to substantial savings in computational cost and memory usage. DPC rules can be integrated with any. Convex ...

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News | Jieping Ye, Ph.D.

http://www.yelab.net/news

Jieping Ye, Ph.D. 14th International Workshop on Data Mining in Bioinformatics. SDM 2015 Workshop on Heterogeneous Learning. SDM 2015 Workshop on Adaptive Learning On-a-chip: Hardware and Algorithms. SDM 2015 Workshop on Machine Learning Methods for Recommender Systems. SDM 2015 Workshop on Big Data for Brain Science. SIAM International Conference on Data Mining, Vancouver, Canada, April 30-May 02, 2015. International Winter School on Big Data, Tarragona, Spain, January 26-30, 2015. MALSAR 1.0 (Multi...

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DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set

DPC: Screening Methods for Sparse Models via Dual Projection onto Convex Set. This package implements the highly effective screening rules — developed under the DPC framework — for many popular sparse learning models, like Lasso, non-negative Lasso, group Lasso, sparse-group Lasso etc. Highly effective in reducing the data size by discarding the irrelevant variables. Highly effective in speeding up any. Existing algorithms for solving the sparse learning models. DPC package 2.1.1.

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