On the best approximation by finite Gaussian mixtures
- URL: http://arxiv.org/abs/2404.08913v1
- Date: Sat, 13 Apr 2024 06:57:44 GMT
- Title: On the best approximation by finite Gaussian mixtures
- Authors: Yun Ma, Yihong Wu, Pengkun Yang,
- Abstract summary: We consider the problem of approximating a general Gaussian location mixture by finite mixtures.
The minimum order of finite mixtures that achieve a prescribed accuracy is determined within constant factors.
- Score: 7.084611118322622
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the problem of approximating a general Gaussian location mixture by finite mixtures. The minimum order of finite mixtures that achieve a prescribed accuracy (measured by various $f$-divergences) is determined within constant factors for the family of mixing distributions with compactly support or appropriate assumptions on the tail probability including subgaussian and subexponential. While the upper bound is achieved using the technique of local moment matching, the lower bound is established by relating the best approximation error to the low-rank approximation of certain trigonometric moment matrices, followed by a refined spectral analysis of their minimum eigenvalue. In the case of Gaussian mixing distributions, this result corrects a previous lower bound in [Allerton Conference 48 (2010) 620-628].
Related papers
- Min-Max Optimization Made Simple: Approximating the Proximal Point
Method via Contraction Maps [77.8999425439444]
We present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems.
Our work is based on the fact that the update rule of the Proximal Point method can be approximated up to accuracy.
arXiv Detail & Related papers (2023-01-10T12:18:47Z) - Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture [0.7499722271664147]
In this paper, we analyze the approximation error between the true entropy and the approximate one to reveal when this approximation works effectively.
Our results provide a guarantee that this approximation works well in higher dimension problems.
arXiv Detail & Related papers (2022-02-26T04:49:01Z) - A Robust and Flexible EM Algorithm for Mixtures of Elliptical
Distributions with Missing Data [71.9573352891936]
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data.
A new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data.
Experimental results on synthetic data demonstrate that the proposed algorithm is robust to outliers and can be used with non-Gaussian data.
arXiv Detail & Related papers (2022-01-28T10:01:37Z) - Clustering a Mixture of Gaussians with Unknown Covariance [4.821312633849745]
We derive a Max-Cut integer program based on maximum likelihood estimation.
We develop an efficient spectral algorithm that attains the optimal rate but requires a quadratic sample size.
We generalize the Max-Cut program to a $k$-means program that handles multi-component mixtures with possibly unequal weights.
arXiv Detail & Related papers (2021-10-04T17:59:20Z) - Mean-Square Analysis with An Application to Optimal Dimension Dependence
of Langevin Monte Carlo [60.785586069299356]
This work provides a general framework for the non-asymotic analysis of sampling error in 2-Wasserstein distance.
Our theoretical analysis is further validated by numerical experiments.
arXiv Detail & Related papers (2021-09-08T18:00:05Z) - Lower Bounds on the Total Variation Distance Between Mixtures of Two
Gaussians [45.392805695921666]
We exploit a connection between total variation distance and the characteristic function of the mixture.
We derive new lower bounds on the total variation distance between pairs of two-component Gaussian mixtures.
arXiv Detail & Related papers (2021-09-02T16:32:16Z) - Spectral clustering under degree heterogeneity: a case for the random
walk Laplacian [83.79286663107845]
This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree.
In the special case of a degree-corrected block model, the embedding concentrates about K distinct points, representing communities.
arXiv Detail & Related papers (2021-05-03T16:36:27Z) - Consistent Estimation of Identifiable Nonparametric Mixture Models from
Grouped Observations [84.81435917024983]
This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations.
A practical implementation is provided for paired observations, and the approach is shown to outperform existing methods.
arXiv Detail & Related papers (2020-06-12T20:44:22Z) - Uniform Convergence Rates for Maximum Likelihood Estimation under
Two-Component Gaussian Mixture Models [13.769786711365104]
We derive uniform convergence rates for the maximum likelihood estimator and minimax lower bounds for parameter estimation.
We assume the mixing proportions of the mixture are known and fixed, but make no separation assumption on the underlying mixture components.
arXiv Detail & Related papers (2020-06-01T04:13:48Z) - Minimax Optimal Estimation of KL Divergence for Continuous Distributions [56.29748742084386]
Esting Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains.
One simple and effective estimator is based on the k nearest neighbor between these samples.
arXiv Detail & Related papers (2020-02-26T16:37:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.