Hyon-Jung Kim

Tampere University | UTA

A new tensor approximation method is developed based on the CANDECOMP/PARAFAC (CP) factorization that enjoys both sparsity (i.e., yielding factor matrices with some nonzero elements) and resistance to outliers and non-Gaussian measurement noise. This method utilizes a robust bounded loss function for errors in the low-rank tensor approximation whil...

Compressed sensing (CS) or sparse signal reconstruction (SSR) is a signal processing technique that exploits the fact that acquired data can have a sparse representation in some basis. One popular technique to reconstruct or approximate the unknown sparse signal is the iterative hard thresholding (IHT) which however performs very poorly under non-G...

We propose novel tensor decomposition methods that advocate both properties of sparsity and robustness to outliers. The sparsity enables us to extract some essential features from a big data that are easily interpretable. The robustness ensures the resistance to outliers that appear commonly in high-dimensional data. We first propose a method that...

Multi-linear techniques using tensor decompositions provide a unifying framework for the high-dimensional data analysis. Sparsity in tensor decompositions clearly improves the analysis and inference of multi-dimensional data. Other than non-negative tensor factorizations, the literature on tensor estimation using sparsity is limited. In this paper,...

Independent component analysis (ICA) is a widely used multivariate analysis technique with applications in many diverse fields such as medical imaging, image processing and data mining. Up to date almost all ICA research have focused on estimation of the mixing and demixing matrix but almost nothing exists on testing hypotheses of the mixing vector...

Assessing regional differences in the survival of cancer patients is important but difficult when separate regions are small or sparsely populated. In this paper, we apply a mixture cure fraction model with random effects to cause-specific survival data of female breast cancer patients collected by the population-based Finnish Cancer Registry. Two...

Despite of the increased interest in independent component analysis (ICA) during the past two decades, a simple closed form expression of the Cramer-Rao bound (CRB) for the demixing matrix estimation has not been established in the open literature. In the present paper we fill this gap by deriving a simple closed-form expression for the CRB of the...

The k-nearest neighbour estimation method is one of the main tools used in multi-source forest inventories. It is a powerful non-parametric method for which estimates are easy to compute and relatively accurate. One downside of this method is that it lacks an uncertainty measure for predicted values and for areas of an arbitrary size. We present a...

The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor effect because the bias is small for small lags. However, fo...

In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to v...

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes...

This paper is motivated by the limited ability of hedonic price equations to deal with spatial variation in house prices. Host (1999) divides spatial processes into low and high frequency components, inspiring the methods developed here. We further divide Host's low frequency spatial patterns into truly low frequency components, typically modeled p...

The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor eect because the bias is small for small lags. However, for...

this paper, we propose to use the tensor product of two one-dimensional tapering functions for spatial data.We also present a method to choose an appropriate smoothing parameter for data tapers and to get better estimates of the spectral density. A good choice of the amount of smoothing, taking into consideration the tradeoff between the bias and t...