Mixture of von Mises-Fisher distribution with sparse prototypes
- URL: http://arxiv.org/abs/2212.14591v1
- Date: Fri, 30 Dec 2022 08:00:38 GMT
- Title: Mixture of von Mises-Fisher distribution with sparse prototypes
- Authors: Fabrice Rossi (CEREMADE), Florian Barbaro (SAMM)
- Abstract summary: Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere.
We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mixtures of von Mises-Fisher distributions can be used to cluster data on the
unit hypersphere. This is particularly adapted for high-dimensional directional
data such as texts. We propose in this article to estimate a von Mises mixture
using a l 1 penalized likelihood. This leads to sparse prototypes that improve
clustering interpretability. We introduce an expectation-maximisation (EM)
algorithm for this estimation and explore the trade-off between the sparsity
term and the likelihood one with a path following algorithm. The model's
behaviour is studied on simulated data and, we show the advantages of the
approach on real data benchmark. We also introduce a new data set on financial
reports and exhibit the benefits of our method for exploratory analysis.
Related papers
- Rejection via Learning Density Ratios [50.91522897152437]
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.
We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
Our framework is tested empirically over clean and noisy datasets.
arXiv Detail & Related papers (2024-05-29T01:32:17Z) - Fast Semisupervised Unmixing Using Nonconvex Optimization [80.11512905623417]
We introduce a novel convex convex model for semi/library-based unmixing.
We demonstrate the efficacy of Alternating Methods of sparse unsupervised unmixing.
arXiv Detail & Related papers (2024-01-23T10:07:41Z) - Learning from aggregated data with a maximum entropy model [73.63512438583375]
We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
arXiv Detail & Related papers (2022-10-05T09:17:27Z) - 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) - A likelihood approach to nonparametric estimation of a singular
distribution using deep generative models [4.329951775163721]
We investigate a likelihood approach to nonparametric estimation of a singular distribution using deep generative models.
We prove that a novel and effective solution exists by perturbing the data with an instance noise.
We also characterize the class of distributions that can be efficiently estimated via deep generative models.
arXiv Detail & Related papers (2021-05-09T23:13:58Z) - A similarity-based Bayesian mixture-of-experts model [0.5156484100374058]
We present a new non-parametric mixture-of-experts model for multivariate regression problems.
Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point.
Posterior inference is performed on the parameters of the mixture as well as the distance metric.
arXiv Detail & Related papers (2020-12-03T18:08:30Z) - GENs: Generative Encoding Networks [4.269725092203672]
We propose and analyze the use of nonparametric density methods to estimate the Jensen-Shannon divergence for matching unknown data distributions to known target distributions.
This analytical method has several advantages: better behavior when training sample quantity is low, provable convergence properties, and relatively few parameters, which can be derived analytically.
arXiv Detail & Related papers (2020-10-28T23:40:03Z) - Graph Embedding with Data Uncertainty [113.39838145450007]
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty.
arXiv Detail & Related papers (2020-09-01T15:08:23Z) - Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers.
We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model.
Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
arXiv Detail & Related papers (2020-06-22T21:12:31Z) - Domain Adaptive Bootstrap Aggregating [5.444459446244819]
bootstrap aggregating, or bagging, is a popular method for improving stability of predictive algorithms.
This article proposes a domain adaptive bagging method coupled with a new iterative nearest neighbor sampler.
arXiv Detail & Related papers (2020-01-12T20:02:58Z)
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.