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 48 for “"Spectral clustering"”.
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Scalable centralized and distributed spectral clustering
Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by the computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the …
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Multilevel spectral clustering : graph partitions and image segmentation
While the spectral graph partitioning method gives high quality segmentation, segmenting large graphs by the spectral method is computationally expensive. Numerous multilevel graph partitioning algorithms are proposed to reduce the segmentation time for the spectral partition of large graphs. …
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Efficient Community Detection for Large Scale Networks via Sub-sampling
… various community detection algorithms such as spectral clustering and extreme point algorithm. One of the main contributions is proposing a new sub-sampling method to make existing spectral clustering method scalable by reducing the computational complexity. Also, we have implemented extreme …
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Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows
… geophysical flows. An updated, parameter-free spectral clustering approach is developed and a noise-based cluster coherence metric is proposed to evaluate the resulting clusters. The method is tested against benchmarks flows of dynamical system theory: the quasi-periodic Bickley jet, the Duffng …
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2D image classification and alignment in single-particle Cryo-EM
… classify images. We also investigate combining spectral clustering with our NUDFT based rotation-invariant features for the image classification and compare its performance with the K-means. We build an algorithm for estimation of rotation between a pair of images on the base of proposed NUDFT. …
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Efficient analysis of data streams
… how data summaries can be used to perform clustering and classification on data streams across a broad range of applications. Spectral clustering is one such technique which prior to this work has not been applicable to the data streaming setting due to the high computation involved. …
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AN INTEGER PROGRAMMING MODEL FOR DYNAMIC TAXI-SHARING CONSIDERING PROVIDER PROFIT
… taxi occupancy rate in real time. A customized spectral clustering approach for preselection on DTS trips is also designed to narrow down the search space for the model. Real-world taxi trip data is used to demonstrate the DTS system is beneficial to providers, taxi users, and taxi drivers.
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Discovery And Visual Analysis of Tracts of Homozygosity In The Human Genome
… discovery. I've designed and implement the TOH clustering algorithm based on repeated binary spectral clustering. A hierarchy of clusters is created and represented by a TOH cluster (TOHC) tree. Researchers can investigate the clusters with a special interactive widget, namely navigation rings, …
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Learning strictly orthogonal p-order nonnegative Laplacian embedding via smoothed iterative reweighted method
… powerful graph based method with its ability in spectral clustering to reveal the intrinsic geometry of data in the high dimensional space. Imposing the orthogonality and the nonnegativity constraints can avoid degenerate and negative solutions, respectively. These two attributes are critical yet …
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A Quantitative Analysis of Women’s Health Investments
… unsupervised learning, particularly KMeans and Spectral Clustering, to identify firms with similar ESG and financial profiles. These clusters reveal latent structures overlooked by traditional industry classifications and support the design of a women’s health-focused investment portfolio. To …
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Clustering Algorithms for Component Placement in Printed Circuit Boards
… we integrated the Louvain and Leiden clustering algorithms for component clustering in PCB placement. We also showed comparative metrics with the spectral clustering algorithm applied to unweighted graph representations, which is the prior state of this project, but it has no knowledge …
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Multi-source contingency clustering
This thesis examines the problem of clustering multiple, related sets of data simultaneously. Given datasets which are in some way connected (e.g. temporally) but which do not necessarily share label compatibility, we exploit co-occurrence in- formation in the form of normalized multidimensional …
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Projection methods for clustering and semi-supervised classification
… on data projection methods for the purposes of clustering and semi-supervised classification, with a primary focus on clustering. A number of contributions are presented which address this problem in a principled manner; using projection pursuit formulations to identify subspaces which contain …
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Quantum Machine Learning Applied to Astronomical Datasets
… unsupervised tasks, including classification, clustering, and anomaly detection. Quantum kernel methods, such as quantum-enhanced support vector machines and quantum-enhanced spectral clustering, as well as quantum variational circuits, including quantum convolutional neural networks and …
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Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations
Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and …
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Learning on Inhomogeneous Hypergraphs
… incorporating EDVWs, based on which I propose spectral partitioning algorithms for co-clustering vertices and hyperedges. Second, I develop a framework for incorporating EDVWs into hypergraph cut problems via introducing a new class of hyperedge splitting functions which are both submodular and …
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Improved methods for fast system reliability analysis through machine-learning-based surrogate models
… clusters is also proposed. The method first uses spectral clustering to partition the network, and estimates the connectivity of these clusters using both a logistic regression and an AdaBoost classifier. Numerical experiments on a California gas distribution network demonstrate that using the …
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Consistent community detection in uni-layer and multi-layer networks
… e.g., the modularity score and (3) based on spectral and matrix factorization methods. In Chapter 2 we consider two random graph models for community detection in multi-layer networks, the multi-layer stochastic block model (MLSBM) and a model with a restricted parameter space, the restricted …
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Observing and quantifying kinematic properties and lagrangian coherent structures of ocean flows using drifter experiments
… vorticity deviation (LAVD), and spectral clustering. This thesis includes the first attempt to apply these dynamical systems techniques to real drifters for LCS detection. Overall, these experiments and the methods used in this paper are shown to be promising new techniques for …
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