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 9 of 9 for “"intrinsic dimensionality"”.
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Reduced-space Gaussian process regression forecast for nonlinear dynamical systems
… the performance of the proposed approach as the intrinsic dimensionality of the system attractor increases in highly turbulent regimes.
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Application of Unsupervised Machine Learning for Event Classification
… fraction with the rapidity spectrum, compute the intrinsic dimensionality for each of the topics, and perform a crosscheck by exploring the tagging performance. The greatest stability and robustness to statistical uncertainties is achieved by a novel method based on parametrizing the endpoints of …
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Design and analysis of algorithms for similarity search based on intrinsic dimension
… collectively referred to as the curse of dimensionality. However, the observed effects of dimensionality in practice may not be as severe as expected. This has led to the development of models quantifying the complexity of data in terms of some measure of the intrinsic dimensionality. The …
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GTM: the generative topographic mapping
… model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing …
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LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS
… data. High dimensional data usually have intrinsic low-dimensional representations, which are suited for subsequent analysis or processing. Therefore, finding low-dimensional representations is an essential step in many machine learning and data mining tasks. Low-rank and sparse modeling …
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Topics in Deep Generative Modelling Mathematical and Computational Aspects of Diffusion Models and Generative Adversarial Networks
… addressing areas like conditional generation, dimensionality estimation, and reduction. Firstly, we examine diffusion models from a mean-field perspective, which leads to a new theoretical insight into the differences between stochastic and deterministic sampling schemes for these models. We …
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Statistical Surrogate Models for Robust Design Optimisation in Reduced Dimension
… surrogates with input uncertainties and intrinsic dimensionality reduction is considered, and the novel reduced dimension variational Gaussian process (RDVGP) surrogate is introduced. The surrogate is trained by fitting to a finite collection of observations from the deterministic …
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Direct and adaptive quantification schemes for extreme event statistics in complex dynamical systems
… transfers, broad energy spectra, and large intrinsic dimensionality, it is largely the case that we are limited to (direct) Monte-Carlo sampling, which is too expensive to apply in real-world applications. To address these challenges, we present both direct and adaptive (sampling based) …
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PATIENT SIMILARITY NETWORKS-BASED METHODS FOR MULTIMODAL DATA INTEGRATION AND CLINICAL OUTCOME PREDICTION
… approaches, -omics data is characterized by high-dimensionality opposed to a limited sample size ("small-sample-size" problem). The high level of sparsity in the resulting datasets often leads to high computational costs and to biased (supervised and unsupervised) analysis, mainly due to redundant …