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Cited By

A Mathematical Framework for Feature Selection from Real-World Data with
Non-Linear Observations

A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations

31 August 2016

ArXiv (abs)PDF HTML

Papers citing "A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations"

3 / 3 papers shown

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization

152

7

0

13 Apr 2018

Neural Factorization Machines for Sparse Predictive Analytics

Neural Factorization Machines for Sparse Predictive Analytics

289

1,379

0

16 Aug 2017

Sparse Proteomics Analysis - A compressed sensing-based approach for
feature selection and classification of high-dimensional proteomics mass
spectrometry data

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry dataBMC Bioinformatics (BMC Bioinformatics), 2015

Christof Schütte

228

35

0

11 Jun 2015

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