Search for dissertations about: "dimensionality reduction"
Showing result 16 - 20 of 51 swedish dissertations containing the words dimensionality reduction.
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16. Manifold learning and representations for image analysis and visualization
Abstract : We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. READ MORE
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17. Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data
Abstract : Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. READ MORE
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18. A New Approach in Profile Analysis with High-Dimensional Data Using Scores
Abstract : In profile analysis, there exist three tests: test of parallelism, test of levels and test of flatness. In this thesis, these tests have been studied. Firstly, a classical setting, where the sample size is greater than the dimension of the parameter space, is considered. READ MORE
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19. Multiscale Stochastic Simulation of Reaction-Transport Processes : Applications in Molecular Systems Biology
Abstract : Quantitative descriptions of reaction kinetics formulated at the stochastic mesoscopic level are frequently used to study various aspects of regulation and control in models of cellular control systems. For this type of systems, numerical simulation offers a variety of challenges caused by the high dimensionality of the problem and the multiscale properties often displayed by the biochemical model. READ MORE
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20. Efficient training of interpretable, non-linear regression models
Abstract : Regression, the process of estimating functions from data, comes in many flavors. One of the most commonly used regression models is linear regression, which is computationally efficient and easy to interpret, but lacks in flexibility. READ MORE