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20122025
most citedProjection Pursuit for non-Gaussian Independent Components

14 citations · 93 across the 23 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.HC2021

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…

math.ST2021

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…

stat.ME2021★ 3 cited

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…

stat.ME2021★ 13 cited

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…

stat.ME2021★ 3 cited

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…

math.ST2021★ 5 cited

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…