Search for dissertations about: "variable reduction"
Showing result 1 - 5 of 209 swedish dissertations containing the words variable reduction.
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1. Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods
Abstract : Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. READ MORE
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2. On subspace-based instrumental variable methods with application to time-varying systems
Abstract : .... READ MORE
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3. The roles of transmission and distribution networks in integrating variable renewable electricity generation
Abstract : Emission reduction targets, together with other factors, such as security of supply, are driving the expansion of variable renewable energy sources for electricity generation, mainly solar and wind power. Trading across the transmission grid is an important measure to handle the increased variability and to balance supply with demand. READ MORE
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4. Modelling the Role of Nuclear Power and Variable Renewables in Climate Change Mitigation
Abstract : As the number of people on Earth and our energy needs have increased the system for providing this energy has become ever more complex and complicated and thus the need for more systematic understanding of it has grown. However, change in energy system is slow and many of the challenges that we face such as mitigating climate change need global solutions. READ MORE
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5. Shared Gaussian Process Latent Variable Models
Abstract : A fundamental task in machine learning is modeling the relationship between different observation spaces. Dimensionality reduction is the task of reducing thenumber of dimensions in a parameterization of a data-set. In this thesis we areinterested in the cross-road between these two tasks: shared dimensionality reduction. READ MORE