Clustering via finite nonparametric ICA mixture models
نویسندگان
چکیده
منابع مشابه
Nonparametric Bayesian Clustering via Infinite Warped Mixture Models
We introduce a flexible class of mixture models for clustering and density estimation. Our model allows clustering of non-linearly-separable data, produces a potentially low-dimensional latent representation, automatically infers the number of clusters, and produces a density estimate. Our approach makes use of two tools from Bayesian nonparametrics: a Dirichlet process mixture model to allow a...
متن کاملNonparametric Estimation of Finite-mixture Models
The aim of this paper is to provide simple nonparametric methods to estimate finite-mixture models from data with repeated measurements. Three measurements suffice for the mixtures to be fully identified and so our approach can be used even with very short panel data. We provide distribution theory for estimators of the number of mixture components, the mixing proportions, as well as of the mix...
متن کاملFinite Mixture Models and Model-Based Clustering
Finite mixture models have a long history in statistics, having been used to model pupulation heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classification. This paper provides a detailed review into mixture models and model-based clustering. Recent trends in the area, as well as open problems are also discussed.
متن کاملNonparametric estimation of multivariate elliptic densities via finite mixture sieves
This paper considers the class of p-dimensional elliptic distributions (p ≥ 1) satisfying the consistency property (Kano, 1994) and within this general framework presents a two-stage semiparametric estimator for the Lebesgue density based on Gaussian mixture sieves. Under the on-line Exponentiated Gradient (EG) algorithm of Helmbold et al. (1997) and without restricting the mixing measure to ha...
متن کاملIdentifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable by introducing a novel framework for clustering overfitted parametric (i.e. misspecified) mixture models. These conditions generalize existing conditions in the literature, and are flexible enough to include for example mixtures of Gaussian mixtures. In...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Data Analysis and Classification
سال: 2018
ISSN: 1862-5347,1862-5355
DOI: 10.1007/s11634-018-0338-x