A Subspace-based Approach for Dimensionality Reduction and Important
Variable Selection

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A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

3 June 2021

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Papers citing "A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection"

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Title
The Adaptive Multi-Factor Model and the Financial Market Liao Zhu 30 13 0 30 Jul 2021
Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model Liao Zhu R. Jarrow M. Wells 36 14 0 09 Nov 2020
Unbiased Measurement of Feature Importance in Tree-Based Methods Zhengze Zhou Giles Hooker 422 64 0 12 Mar 2019
High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model Liao Zhu Sumanta Basu R. Jarrow M. Wells 61 23 0 23 Apr 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes John Healy James Melville 202 9,479 0 09 Feb 2018
Using stacking to average Bayesian predictive distributions Yuling Yao Aki Vehtari Daniel P. Simpson Andrew Gelman 105 341 0 06 Apr 2017
Robustness of Random Forest-based gene selection methods M. Kursa 74 264 0 20 May 2013