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 10 of 10 for “"Non-Linear Dimensionality Reduction"”.
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First Principles Dynamics and Coarse-Grained Characterization of Photoisomerization in Complex Environments
… necessarily apply. To quantify the rates of such non-TST reactions, we have applied non-linear dimensionality reduction techniques to dynamical simulation data. Such techniques permit definition of simple, 1-dimensional reaction paths directly from dynamical simulation, without recourse to TST. …
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Data Reduction in Smart Grid
… Therefore, it is necessary to implement a data reduction technique that would not only curtail the exponential data growth, but also be able to make smart decisions with the reduced data.This thesis deals with two data reduction techniques; data compression and dimensionality reduction. Some of …
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A Data-Driven Approach to Improve Optical Fiber Manufacturing: Focus on Core Deposition
… learning, process 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 …
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Experimental Investigations of Internal Air-water Flows
… flows. These techniques were automated, non-intrusive and economical, which ensured that their use would be feasible in industrial as well as laboratory settings. Measurements of differential pressure and the gas and liquid flow rates were collected in vertical upwards air-water flow at …
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Robust and interpretable high-dimensional machine learning for predictive cancer medicine
… data is challenging and often relies upon dimensionality reduction techniques. These methods reveal structure within data, potentially exposing meaningful biological patterns. In predictive cancer medicine, it is common to employ linear dimensionality reduction methods due to their inherent …
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IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION
… The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear …
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Machine-Learning-Based Approach to Decoding Physiological and Neural Signals
… is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals.</p> <p>Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction …
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ISO-maps for Non-linear Dimension Reduction - Addressing Geometric and Numerical Issues Observed in Practice
… data has necessitated various approaches to dimensionality reduction. Many of these methods make simplifying assumptions about the variation structure of the data and permit approximating high-dimensional structures using only a few components. In the context of modern day machine learning …
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A Probabilistic Approach To Multiple-Instance Learning
This study introduced a probabilistic approach to the multiple-instance learning (mil) problem. In particular, two bayes classication algorithms were proposed where posterior probabilities were estimated under dierent assumptions. The rst algorithm, named instance-vote, assumes that the probability …
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Joint-stochastic Spectral Inference for Robust Co-occurrence Modeling and Latent Topic Analysis
… fast algorithms and optimality guarantees for non-linear dimensionality reduction or latent topic analysis. Spectral approaches reduce the dependence on the original training examples and produce substantial gain in efficiency, but at costs: a) The algorithms perform poorly on real data that …