Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Dimensionality reduction algorithms"”.
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Classifying RNA secondary structures using support vector machines
… cluster them. The proposal is to apply existing dimensionality reduction algorithms to these extracted structures and then cluster them in a reduced dimensional space using Support Vector Machines.
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Dimensionality reduction for k-means clustering
In this thesis we study dimensionality reduction techniques for approximate k-means clustering. Given a large dataset, we consider how to quickly compress to a smaller dataset (a sketch), such that solving the k-means clustering problem on the sketch will give an approximately optimal solution on …
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Unsupervised learning of vocal tract sensory-motor synergies
… or walking gaits have. Motivated by the use of dimensionality reduction algorithms in learning muscle synergies and perceptual primitives that reflect the structure in biological systems, an approach to learning sensory-motor synergies via dynamic factor analysis for control of a simulated vocal …
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A Data-Driven Approach to Improve Optical Fiber Manufacturing: Focus on Core Deposition
… by process, we applied linear and non linear dimensionality reduction algorithms (PCA and t-sne) to features matrices created from time series data and have been able to connect data clusters with context information like machines or month of the year. Then considering the core fabrication …
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Graph Embedding and Nonlinear Dimensionality Reduction
… have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure. However, these methods often perform poorly when applied to data which does not lie exactly near a linear …
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Design Optimisation and Flow Characterisation for Future Aeroengine Axial Fan Blades
… computational cost of employing global search algorithms on such scenarios has typically been prohibitive for most academic and industrial environments. In this work, a novel methodology is presented, called AInADS. This strategy leverages the capabilities of Artificial Neural Networks for …