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Smoothing Proximal Gradient Method for General Structured Sparse Learning

Smoothing Proximal Gradient Method for General Structured Sparse Learning

Smoothing Proximal Gradient Method for General Structured Sparse Learning. Eric Xing epxing@cs.cmu.edu School of Computer Science Carnegie Mellon University Joint work with: Xi Chen, Qihang Lin, Seyoung Kim, Jaime Carbonell. Moder n High-Dimensional Problems. Genomics:

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779 views • 40 slides
Spectral Feature Selection for Mining Ultrahigh Dimensional Data

Spectral Feature Selection for Mining Ultrahigh Dimensional Data

Spectral Feature Selection for Mining Ultrahigh Dimensional Data. Zheng (Alan) Zhao Computer Science and Engineering Arizona State University. Massive Data and High Dimensionality. Dimensionality of data has increased exponentially. log. Pervasive Phenomenon.

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683 views • 38 slides
Thesis Proposal Learning with Sparsity: Structures, Optimization and Applications

Thesis Proposal Learning with Sparsity: Structures, Optimization and Applications

Thesis Proposal Learning with Sparsity: Structures, Optimization and Applications. Xi Chen Committee Members: Jaime Carbonell (chair), Tom Mitchell, Larry Wasserman, Robert Tibshirani Machine Learning Department Carnegie Mellon University. Modern Data Analysis.

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705 views • 37 slides
Efficient and Numerically Stable Sparse Learning

Efficient and Numerically Stable Sparse Learning

Efficient and Numerically Stable Sparse Learning. Sihong Xie 1 , Wei Fan 2 , Olivier Verscheure 2 , and Jiangtao Ren 3 1 University of Illinois at Chicago, USA 2 IBM T.J. Watson Research Center, New York, USA 3 Sun Yat-Sen University, Guangzhou, China. Applications.

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283 views • 20 slides
An Efficient Algorithm for a Class of Fused Lasso Problems

An Efficient Algorithm for a Class of Fused Lasso Problems

An Efficient Algorithm for a Class of Fused Lasso Problems. Jun Liu, Lei Yuan, and Jieping Ye Computer Science and Engineering The Biodesign Institute Arizona State University. Sparse Learning for Feature Selection. features. min loss( x ) + penalty( x ). Data points. x.

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220 views • 20 slides

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