62 citations · 615 across the 49 of their papers we have counts for
22 papers · 1 filter
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,…
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…
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…
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…
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…
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…
Notion of information and independent component analysis
Una Radojicic, Klaus Nordhausen, Hannu Oja
Partial orderings and measures of information for continuous univariate random variables with special roles of Gaussian and uniform distributions are discussed. The information mea…
Spatial Blind Source Separation
François Bachoc, Marc G. Genton, Klaus Nordhausen +2
Recently a blind source separation model was suggested for spatial data together with an estimator based on the simultaneous diagonalisation of two scatter matrices. The asymptotic…
Extracting conditionally heteroscedastic components using ICA
Jari Miettinen, Markus Matilainen, Klaus Nordhausen +1
In the independent component model, the multivariate data is assumed to be a mixture of mutually independent latent components, and in independent component analysis (ICA) the aim…
Determining the signal dimension in second order source separation
Joni Virta, Klaus Nordhausen
While an important topic in practice, the estimation of the number of non-noise components in blind source separation has received little attention in the literature. Recently, two…
Fast tensorial JADE
Joni Virta, Niko Lietzén, Pauliina Ilmonen +1
In this work, we propose a novel method for tensorial independent component analysis. Our approach is based on TJADE and -JADE, two recently proposed generalizations of the cl…
Independent component analysis for multivariate functional data
Joni Virta, Bing Li, Klaus Nordhausen +1
We extend two methods of independent component analysis, fourth order blind identification and joint approximate diagonalization of eigen-matrices, to vector-valued functional data…
Blind source separation of tensor-valued time series
Joni Virta, Klaus Nordhausen
The blind source separation model for multivariate time series generally assumes that the observed series is a linear transformation of an unobserved series with temporally uncorre…
Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace
Klaus Nordhausen, Hannu Oja, David E. Tyler +1
Dimension reduction is often a preliminary step in the analysis of large data sets. The so-called non-Gaussian component analysis searches for a projection onto the non-Gaussian pa…
Projection Pursuit for non-Gaussian Independent Components
Joni Virta, Klaus Nordhausen, Hannu Oja
In independent component analysis it is assumed that the observed random variables are linear combinations of latent, mutually independent random variables called the independent c…
JADE for Tensor-Valued Observations
Joni Virta, Bing Li, Klaus Nordhausen +1
Independent component analysis is a standard tool in modern data analysis and numerous different techniques for applying it exist. The standard methods however quickly lose their e…
Independent component analysis for tensor-valued data
Joni Virta, Bing Li, Klaus Nordhausen +1
In preprocessing tensor-valued data, e.g. images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to one of the many methods used f…
The squared symmetric FastICA estimator
Jari Miettinen, Klaus Nordhausen, Hannu Oja +2
In this paper we study the theoretical properties of the deflation-based FastICA method, the original symmetric FastICA method, and a modified symmetric FastICA method, here called…
Joint Use of Third and Fourth Cumulants in Independent Component Analysis
Joni Virta, Klaus Nordhausen, Hannu Oja
The independent component model is a latent variable model where the components of the observed random vector are linear combinations of latent independent variables. The aim is to…
Fourth Moments and Independent Component Analysis
Jari Miettinen, Sara Taskinen, Klaus Nordhausen +1
In independent component analysis it is assumed that the components of the observed random vector are linear combinations of latent independent random variables, and the aim is the…
Separation of uncorrelated stationary time series using autocovariance matrices
Jari Miettinen, Katrin Illner, Klaus Nordhausen +3
Blind source separation (BSS) is a signal processing tool, which is widely used in various fields. Examples include biomedical signal separation, brain imaging and economic time se…