7 citations · 30 across the 11 of their papers we have counts for
12 papers
New M-estimator of the leading principal component
Joni Virta, Una Radojicic, Marko Voutilainen
We study the minimization of the non-convex and non-differentiable objective function in . In particula…
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,…
Dimension estimation in PCA model using high-dimensional data augmentation
Una Radojicic, Joni Virta
We propose a modified, high-dimensional version of a recent dimension estimation procedure that determines the dimension via the introduction of augmented noise variables into the…
Robust covariance estimation and explainable outlier detection for matrix-valued data
Marcus Mayrhofer, Una Radojičić, Peter Filzmoser
This work introduces the Matrix Minimum Covariance Determinant (MMCD) method, a novel robust location and covariance estimation procedure designed for data that are naturally repre…
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…
Minimum regularized covariance trace estimator and outlier detection for functional data
Jeremy Oguamalam, Una Radojičić, Peter Filzmoser
In this paper, we propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection. The MRCT esti…
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…
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…
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…
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…