Robust regression via error tolerance

Real-world datasets are often characterised by outliers; data items that do not follow the same structure as the rest of the data. These outliers might negatively influence modelling of the data. In data analysis it is, therefore, important to consider methods that are robust to outliers. In this paper we develop a robust regression method that finds the largest subset of data items that can be approximated using a sparse linear model to a given precision. We show that this can yield the best possible robustness to outliers. However, this problem is NP-hard and to solve it we present an efficient approximation algorithm, termed SLISE. Our method extends existing state-of-the-art robust regression methods, especially in terms of speed on high-dimensional datasets. We demonstrate our method by applying it to both synthetic and real-world regression problems.Peer reviewe

Demonstration of combustion of grain drying by-products and wood chips fuel mix in a heating boiler

Promoting renewable energy projects are strongly linked to national goals to curb the use of fossil fuels and to help move towards a carbon-neutral society in Finland. One objective in the national development project “Changing rural energy production” is to increase the utilization rate of agricultural by-products in energy production in the province of Southern Ostrobothnia. In the project, by-product of grain drying was briquetted and was tested as fuel in conventional heating boiler for solid bio fuels with chips. The aim of the combustion tests was to find out the effect of the spent fuel mixture on the efficiency and emissions of a conventional bio heating boiler. Five different combustion tests were performed on the grain fraction, the variables being the share of the grain fraction in the incineration of the chips and the use of the grain fraction unprocessed and briquetted. The tests were performed at the boiler testing laboratory of the Bioeconomy Institute of Jamk University of Applied Sciences (Jamk) in Saarijärvi, Central Finland. The experiments showed that the increase in grain by-product fraction in the fuel mix in combustion did not clearly affect the efficiency of the boiler. However, the increased share of grain fraction did result higher NOx emission and dust levels

Explaining any black box model using real data

Peer reviewe

Siipikarjanlannan käytön tehostaminen : Teholanta-hankkeen loppuraportti

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