Filter and export

Publications

What is a Publication?
69 Publications visible to you, out of a total of 69
Linear Dimensionality Reduction for Time Series

Abstract (Expand)

Visualisation by dimensionality reduction is an important tool for data exploration. In this work we are interested in visualising time series. To that end we formulate a latent variable model that … mirrors probabilistic principal component analysis (PPCA). However, as opposed to PPCA which maps the latent variables directly to the data space, we first map the latent variables to the parameter space of a recurrent neural network, i.e. each latent projection instantiates a recurrent network. Each instantiated recurrent network in turn is responsible for modelling a time series in the dataset. Hence, each latent variable is indirectly mapped to a time series. Incorporating the recurrent network in the latent variable model helps us account for the temporal nature of the time series and capture their underlying dynamics. The proposed algorithm is demonstrated on two benchmark problems and a real world dataset.

Author: Nikolaos Gianniotis

Date Published: 2017

Publication Type: InProceedings

A spectral model for multimodal redshift estimation

Abstract

Not specified

Authors: Sven D. Kugler, Nikolaos Gianniotis, Kai L. Polsterer

Date Published: 1st Dec 2016

Publication Type: InProceedings

Model-coupled autoencoder for time series visualisation

Abstract

Not specified

Authors: Nikolaos Gianniotis, Sven D. Kügler, Peter Tiňo, Kai L. Polsterer

Date Published: 1st Jun 2016

Publication Type: Journal

Approximate variational inference based on a finite sample of Gaussian latent variables

Abstract

Not specified

Authors: Nikolaos Gianniotis, Christoph Schnörr, Christian Molkenthin, Sanjay Singh Bora

Date Published: 1st May 2016

Publication Type: Journal

An explorative approach for inspecting Kepler data

Abstract

Not specified

Authors: Sven D. Kügler, Nikolaos Gianniotis, Kai L. Polsterer

Date Published: 2016

Publication Type: Journal

Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs

Abstract

Not specified

Authors: Kai L. Polsterer, Fabian Gieseke, Christian Igel, Bernd Doser, Nikolaos Gianniotis

Date Published: 2016

Publication Type: InProceedings

On the application of machine learning approaches in astronomy: Exploring novel representations of high-dimensional and complex astronomical data

Abstract (Expand)

The goal of the presented work is the application of data-driven methods on complex and high- dimensional astronomical databases. The focus of the work is the exploration of novel data representations … in order to enable the use of statistical learning approaches in the analysis of data. With the help of diverse science cases, the advantages of the introduced approaches for classication, visualization and regression tasks are shown by applying the developed methodology to astronomical data. In the first part, an alternative approach for estimating redshifts of spectra by using the knowledge about the redshifts provided by the SDSS pipeline is presented. A novel data repre- sentation is employed which contains only information relevant for estimating the redshift and the detection of multiple redshift systems. Subsequently, a novel data representation for regu- larly sampled light curves based on recurrent networks is presented. This allows an explorative investigation of huge databases with unlabeled data. Finally, a new way of representing the static part of irregularly sampled light curves by a mixture of Gaussians is discussed. This represen- tation is more general than the extraction of features, as it allows the inclusion of photometric uncertainties and avoids the introduction of observational biases.

Author: Sven Dennis Kugler

Date Published: 9th Dec 2015

Publication Type: Doctoral Thesis

Powered by
 (v.1.15.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH