mlsanityData to 7 Oct 2026

Kernel Feature Selection via Conditional Covariance Minimization

arXiv 1707.01164 ↗First posted 4 Jul 2017stat.ML cs.AI cs.LGcited by 20

Not on the map. Why?

Abstract (arXiv)

We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response. Building on past work in kernel dimension reduction, we show how to perform feature selection via a constrained optimization problem involving the trace of the conditional covariance operator. We prove various consistency results for this procedure, and also demonstrate that our method compares favorably with other state-of-the-art algorithms on a variety of synthetic and real data sets.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 2; 2025: 5.

20 papers.

Sort:

By area:

  1. 2021How rotational invariance of common kernels prevents generalization in high dimensionscited by 32
  2. 2023The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importancecited by 29
  3. 2022Where to Pay Attention in Sparse Training for Feature Selection?cited by 15
  4. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  5. 2021Efficient Decompositional Rule Extraction for Deep Neural Networkscited by 11
  6. 2023Contextual Feature Selection with Conditional Stochastic Gatescited by 6
  7. 2022Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Datacited by 5
  8. 2022Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavitiescited by 3
  9. 2023Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networkscited by 2
  10. 2025On the Complexity-Faithfulness Trade-off of Gradient-Based Explanationscited by 2
  11. 2022An Embedded Feature Selection Framework for Controlcited by 1
  12. 2025Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learningcited by 1
8 more
  1. 2022Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selectioncited by 0
  2. 2022RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Featurescited by 0
  3. 2023End-to-end Feature Selection Approach for Learning Skinny Treescited by 0
  4. 2024EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Featurescited by 0
  5. 2024On the Limitation of Kernel Dependence Maximization for Feature Selectioncited by 0
  6. 2025ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learningcited by 0
  7. 2025Where to Intervene: Action Selection in Deep Reinforcement Learningcited by 0
  8. 2025A Compositional Kernel Model for Feature Learningcited by 0