Add to ORKGWe present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimen-sional, multimodal distribution. In addition, we allow the components of latent variable models to be drawn from the exponential family which makes the method suitable for special data types, for example binary or count data. Simulations on real valued, binary and count data show fa-vorable comparison to other related schemes both in terms of separating different populations and generalization to unseen samples.
In this paper we introduce a new underlying probabilistic model for prin-cipal component analysis (P...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
We consider first a semi-nonparametric specification for the density of latent variables in Generali...
Principal component analysis is a widely used technique for dimensionality reduction, but is not bas...
Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this pape...
This dissertation considers the problem of learning the underlying statistical structure of complex ...
Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA...
Collins, Dasgupta, and Shcapire present a way to generalize the popuar di-mensionality reduction met...
Summary. Exponential principal component analysis (e-PCA) provides a frame-work for appropriately de...
We propose a bivariate exponential model with exponential marginal densities, correlated via a laten...
Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA...
We consider a semi-nonparametric specification for the density of latent variables in Generalized Li...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
Summarising a high dimensional data set with a low dimensional embedding is a standard approach for ...
Graphical models are commonly used tools for modeling multivariate random variables. While there exi...
In this paper we introduce a new underlying probabilistic model for prin-cipal component analysis (P...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
We consider first a semi-nonparametric specification for the density of latent variables in Generali...
Principal component analysis is a widely used technique for dimensionality reduction, but is not bas...
Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this pape...
This dissertation considers the problem of learning the underlying statistical structure of complex ...
Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA...
Collins, Dasgupta, and Shcapire present a way to generalize the popuar di-mensionality reduction met...
Summary. Exponential principal component analysis (e-PCA) provides a frame-work for appropriately de...
We propose a bivariate exponential model with exponential marginal densities, correlated via a laten...
Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA...
We consider a semi-nonparametric specification for the density of latent variables in Generalized Li...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
Summarising a high dimensional data set with a low dimensional embedding is a standard approach for ...
Graphical models are commonly used tools for modeling multivariate random variables. While there exi...
In this paper we introduce a new underlying probabilistic model for prin-cipal component analysis (P...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
We consider first a semi-nonparametric specification for the density of latent variables in Generali...