Detection and Feature Selection in Sparse Mixture Models
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Abstract (arXiv)
We consider Gaussian mixture models in high dimensions and concentrate on the twin tasks of detection and feature selection. Under sparsity assumptions on the difference in means, we derive information bounds and establish the performance of various procedures, including the top sparse eigenvalue of the sample covariance matrix and other projection tests based on moments, such as the skewness and kurtosis tests of Malkovich and Afifi (1973), and other variants which we were better able to control under the null.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2014.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 2.
6 papers.
- 2024Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Datacited by 3
- 2022Support Recovery in Mixture Models with Sparse Parameterscited by 2
- 2023Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learningcited by 2
- 2024On Parameter Estimation in Deviated Gaussian Mixture of Expertscited by 2
- 2022Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gapcited by 1
- 2023Subject clustering by IF-PCA and several recent methodscited by 1
- 2024Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data5 Sep 2024
- 2024On Parameter Estimation in Deviated Gaussian Mixture of Experts7 Feb 2024
- 2023Subject clustering by IF-PCA and several recent methods8 Jun 2023
- 2023Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning18 Apr 2023
- 2022Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap26 May 2022
- 2022Support Recovery in Mixture Models with Sparse Parameters24 Feb 2022