mlsanityData to 7 Oct 2026

Support Recovery in Mixture Models with Sparse Parameters

arXiv 2202.11940 ↗First posted 24 Feb 2022cs.LG cs.IT math.ITcited by 2

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Abstract (arXiv)

Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse latent parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixture models is well-studied, the sparsity constraint remains relatively unexplored. Sparsity of parameter vectors is a natural constraint in variety of settings, and support recovery is a major step towards parameter estimation. We provide efficient algorithms for support recovery that have a logarithmic sample complexity dependence on the dimensionality of the latent space. Our algorithms are quite general, namely they are applicable to 1) mixtures of many different canonical distributions including Uniform, Poisson, Laplace, Gaussians, etc. 2) Mixtures of linear regressions and linear classifiers with Gaussian covariates under different assumptions on the unknown parameters. In most of these settings, our results are the first guarantees on the problem while in the rest, our results provide improvements on existing works.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 24 Feb 2022.

Builds on

20 of 49 references.

References · 20

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8 more
  1. 2014Detection and Feature Selection in Sparse Mixture Modelscited by 6
  2. 2020Uniform Convergence Rates for Maximum Likelihood Estimation under Two-Component Gaussian Mixture Modelscited by 6
  3. 2020Recovery of Sparse Signals from a Mixture of Linear Samplescited by 6
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  8. 2016A Simple Approach to Sparse Clusteringcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distributioncited by 1
  2. 2026Efficient Support Recovery of Mixtures of Sparse Linear Classifiers with Fewer Measurementscited by 0