FANOK: Knockoffs in Linear Time
We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large scale feature selection problems. Identifying the knockoff distribution requires solving a large scale semidefinite program for which we derive several efficient methods. One handles generic covariance matrices, has a complexity scaling as $O(p^3)$ where $p$ is the ambient dimension, while another assumes a rank $k$ factor model on the covariance matrix to reduce this complexity bound to $O(pk^2)$. We also derive efficient procedures to both estimate factor models and sample knockoff covariates with complexity linear in the dimension. We test our methods on problems with $p$ as large as $500,000$.
Code (1)
Tasks
feature selectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs
Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the …
Asymptotic FDR Control with Model-X Knockoffs: Is Moments Matching Sufficient?
We propose a unified theoretical framework for studying the robustness of the model-X knockoffs framework by investigating the asymptotic false discovery rate (FDR) control of the practically implemented approximate knoc…
ARK: Robust Knockoffs Inference with Coupling
We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution. We achieve such a goal by theoretically studying the feature selection performance of a…
feature selectionKnockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with…
Image ClassificationLearning with noisy labelsregressionImproving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization
The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoff is that if we have a good model of the features X, then we can ident…
feature selection