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 “"spectral embedding"”.
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Robust and scalable unsupervised learning via landmark diffusion, from theory to medical application
… by such challenging task, we proposed a novel spectral embedding algorithm, which we coined Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). The solution is a generic and not limited to analyze physiological waveforms. In short, we measure the affinity between two points via a …
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Graph-Based Acoustic Clustering and Classification
… processing and problems on networks, we apply a spectral embedding to project the high-dimensional graph data onto a low-dimensional subspace. We show how the Nyström extension can accelerate the calculation of the eigenvectors of the graph Laplacian, and how to adapt the method to accommodate …
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Cerebral white matter analysis using diffusion imaging
… is represented as a point in a high-dimensional spectral embedding space, and common structures are found by clustering in this space. By annotating the clusters with anatomical labels, we create a model that we call a high-dimensional white matter atlas.
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Learning strictly orthogonal p-order nonnegative Laplacian embedding via smoothed iterative reweighted method
Laplacian embedding is a 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 …
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Uncovering latent structure in social networks using graph embeddings
… and then embedded in geometric spaces using spectral embedding techniques. Communities and clusters in these geometric spaces correspond to groups of users with similar interests, and such groups can be used for, for example, targeted marketing, content recommendations, and creating a higher …
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Visual attention models for far-field scene analysis
… are grouped together. We group the tracks using spectral clustering and represent the scene model as a mixture of Gaussians in the spectral embedding space. New examples of activity can be efficiently classified by projection into the embedding space. We demonstrate clustering and unusual …
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Communities in Social Networks: Detection, Heterogeneity and Experimentation
… Existing literature has focused on modeling the spectral embedding of a network using Gaussian mixture models (GMMs) in scaling regimes where the ability to detect community memberships improves with the size of the network. However, these regimes are not very realistic. As such, we provide …
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Inference of Low-Dimensional Latent Structure in High-Dimensional Data
… are employed to learn a reversible statistical embedding. The proposed embedding procedure is connected to spectral embedding methods, for example, diffusion maps and Isomap, yielding a new statistical spectral framework. The proposed approach allows one to discard the training data when …
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Study of the Diffusion Map method in the context of social science data sets
… the Diffusion Map and compares them with other spectral methods. It investigates the impact of the Diffusion Map parameters as well as the structure of the underlying data on the results. The V-Dem democracy dataset, British census data, and data on German urban and rural districts are then …