62 citations · 615 across the 49 of their papers we have counts for
51 papers
Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that the…
Unsupervised linear discrimination using skewness
Una Radojicic, Klaus Nordhausen, Joni Virta
It is well-known that, in Gaussian two-group separation, the optimally discriminating projection direction can be estimated without any knowledge on the group labels. In this work,…
Independent vector analysis -- an introduction for statisticians
Miro Arvila, Klaus Nordhausen, Mika Sipilä +1
Blind source separation (BSS), particularly independent component analysis (ICA), has been widely used in various fields of science such as biomedical signal processing to recover…
Modelling multivariate spatio-temporal data with identifiable variational autoencoders
Mika Sipilä, Claudia Cappello, Sandra De Iaco +2
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated fro…
Invariant Coordinate Selection and Fisher discriminant subspace beyond the case of two groups
Colombe Becquart, Aurore Archimbaud, Anne Ruiz-Gazen +2
Invariant Coordinate Selection (ICS) is a multivariate technique that relies on the simultaneous diagonalization of two scatter matrices. It serves various purposes, including its…
Nonlinear blind source separation exploiting spatial nonstationarity
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover laten…
Data Type Agnostic Visual Sensitivity Analysis
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +4
Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial da…
The Asymptotic Properties of the One-Sample Spatial Rank Methods
Jyrki Möttönen, Klaus Nordhausen, Hannu Oja +1
For a set of -variate data points , there are several versions of multivariate median and related multivariate sign test proposed and stu…
Robust and Resistant Regularized Covariance Matrices
David E. Tyler, Mengxi Yi, Klaus Nordhausen
We introduce a class of regularized M-estimators of multivariate scatter and show, analogous to the popular spatial sign covariance matrix (SSCM), that they possess high breakdown…
Tandem clustering with invariant coordinate selection
Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen +1
For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach us…
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
François Bachoc, Christoph Muehlmann, Klaus Nordhausen +1
Non-stationary source separation is a well-established branch of blind source separation with many different methods. However, for none of these methods large-sample results are av…
Approximating Symmetrized Estimators of Scatter via Balanced Incomplete U-Statistics
Lutz Duembgen, Klaus Nordhausen
We derive limiting distributions of symmetrized estimators of scatter, where instead of all pairs of the observations we only consider suitably chosen pairs, $1…
Order Determination for Tensor-valued Observations Using Data Augmentation
Una Radojicic, Niko Lietzen, Klaus Nordhausen +1
Tensor-valued data benefits greatly from dimension reduction as the reduction in size is exponential in the number of modes. To achieve maximal reduction without loss in informatio…
Numerical considerations and a new implementation for ICS
Aurore Archimbaud, Zlatko Drmač, Klaus Nordhausen +2
Invariant Coordinate Selection (ICS) is a multivariate data transformation and a dimension reduction method that can be useful in many different contexts. It can be used for outlie…
Visual Parameter Selection for Spatial Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +3
Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to m…
Kurtosis-based projection pursuit for matrix-valued data
Una Radojicic, Klaus Nordhausen, Joni Virta
We develop projection pursuit for data that admit a natural representation in matrix form. For projection indices, we propose extensions of the classical kurtosis and Mardia's mult…
Spatial Blind Source Separation in the Presence of a Drift
Christoph Muehlmann, Peter Filzmoser, Klaus Nordhausen
Multivariate measurements taken at different spatial locations occur frequently in practice. Proper analysis of such data needs to consider not only dependencies on-sight but also…
Blind source separation for non-stationary random fields
Christoph Muehlmann, François Bachoc, Klaus Nordhausen
Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely…
Stationary subspace analysis based on second-order statistics
Lea Flumian, Markus Matilainen, Klaus Nordhausen +1
In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p-k)-variate station…
Large-Sample Properties of Blind Estimation of the Linear Discriminant Using Projection Pursuit
Una Radojicic, Klaus Nordhausen, Joni Virta
We study the estimation of the linear discriminant with projection pursuit, a method that is blind in the sense that it does not use the class labels in the estimation. Our viewpoi…
TBSSvis: Visual Analytics for Temporal Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Theresia Gschwandtner +4
Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to…
Test of the Latent Dimension of a Spatial Blind Source Separation Model
Christoph Muehlmann, François Bachoc, Klaus Nordhausen +1
We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated Gaussian random fields contai…
Non-Gaussian component analysis: testing the dimension of the signal subspace
Una Radojicic, Klaus Nordhausen
Dimension reduction is a common strategy in multivariate data analysis which seeks a subspace which contains all interesting features needed for the subsequent analysis. Non-Gaussi…
Sliced Inverse Regression for Spatial Data
Christoph Muehlmann, Hannu Oja, Klaus Nordhausen
Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recent…