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 20 of 39 for “"Manifold learning"”.
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Manifold Learning From Time Series
We apply our manifold learning algorithm to synthetic data and real world applications. The experiment on synthetic data clearly demonstrates that by taking temporal dependency among global coordinates into consideration our proposed algorithm achieves superior learning results than other manifold …
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Visualising Energy Landscapes Through Manifold Learning
… by adapting existing, state-of-the-art manifold learning approaches to better deal with the structural datasets obtained from searching. Also discussed is a method for basin volume computation, previously applied to model periodic systems with fixed unit cells. Adapting this method to …
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Making Hands: Neural Implicit Manifold Learning of Hand Gestures
… space of hands as a high-dimensional manifold via neural unsigned distance fields, and I define plausible hand poses as points on the manifold. Next, I apply a distance metric to their configuration space. A trajectory in that space is a finite or infinite sequence of hand poses. These …
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Manifold learning based spectral unmixing of hyperspectral remote sensing data
… and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing …
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Regularized algorithms for ranking, and manifold learning for related tasks
… retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related …
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Exploring the dimensionality of speech using manifold learning and dimensionality reduction methods
… applied to speech. However, if speech lies on a manifold nonlinearly embedded in high-dimensional space, as has been proposed in the past, classic linear dimensionality reduction methods would be unable to discover this embedding. In this dissertation a number of manifold learning, also referred …
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Manifold learning techniques and statistical approaches applied to the disruption prediction in tokamaks
… used to describe the plasma operational space. Manifold learning algorithms attempt to identify these structures in order to find a low-dimensional representation of the data. Data for this thesis comes from ASDEX Upgrade (AUG). ASDEX Upgrade is a medium size tokamak experiment located at IPP …
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Nonlinear machine learning of macromolecular folding and self-assembly
… computation and sophisticated machine learning algorithms have emerged as new tools for studying biological, physical and chemical systems at the atomistic scale. In this thesis, I report several applications of molecular dynamics simulation and machine learning in the study of the …
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (\ie, item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms …
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Learning manifolds with the Parametrized Self-Organizing Map and Unsupervised Kernel Regression
… several new developments in the field of manifold learning and nonlinear dimensionality reduction. The main text can be divided into three parts, the first of which presents a smoothness-based regularizer that is specifically tuned to the Parametrized Self-Organizing Map (PSOM). The …
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Manifold Sculpting
Manifold learning algorithms have been shown to be useful for many applications of numerical analysis. Unfortunately, existing algorithms often produce noisy results, do not scale well, and are unable to benefit from prior knowledge about the expected results. We propose a new algorithm that …
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Similarity modeling for machine learning
… is an important topic for both machine learning and computer vision. In this dissertation, we first propose a discriminative similarity learning method, then introduce two novel sparse similarity modeling methods for high dimensional data from the perspective of manifold learning and …
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Learning to transform time series with a few examples
… for each tracking task separately, I suggest learning a memoryless transformations of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that …
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On Motion Parameterizations in Image Sequences from Fixed Viewpoints
… for a new class of Partially Unsupervised Manifold Learning: PUML) problems, which often arise in medical imagery. Specifically, we create energy functions for measuring how consistent a given velocity vector is with observed spatio-temporal image derivatives. These energy functions are …
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Interpretable Physics-informed Machine Learning Methods for Scientific Modeling and Data Analysis
With the recent advancement of modern machine learning methods, there are now many exciting opportunities to use machine learning in scientific research, including for modeling and data analysis. Machine learning has the potential to become an indispensable tool for scientific discovery, but it is …
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Manifold aligned density estimation
… new challenges for some traditional machine learning tasks. This thesis is mainly concerned with manifold aligned density estimation problems. In particular, the work presented in this thesis includes efficiently learning the density distribution on very large-scale datasets and estimating …
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From cognitive to docitive radios: the role of machine learning in intelligent wireless multimedia networks
… networking techniques and cutting-edge machine learning techniques. Particularly, crosslayer design for multimedia transmission, spectrum handoff for cognitive radio networks, and multichannel wireless mesh networks with multi-beam antennas are addressed for the improvement of multimedia …
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Dense Optical Flow Estimation using Diffusion Distances
… diffusion framework and its predecessors in the manifold learning literature. Local image features are recorded by diffusion distances calculated from the graph Laplacian whose kernel function depends on inter-pixel intensity differences in a certain neighbourhood. These features are then used in …
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