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 7 of 7 for “"Subspace clustering"”.
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Robust learning with low-dimensional structure: theory,algorithms and applications
… that are common in real problems: ?low-rank subspace model? that underlies matrix completion and Robust PCA, and ?union-of-subspace model? that arises in the problem of subspace clustering. In the upcoming chapters, we will present (i) stability of matrix factorization and its consequences in …
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Criminal data analysis based on low rank sparse representation
FINDING effective clustering methods for a high dimensional dataset is challenging due to the curse of dimensionality. These challenges can usually make the most of basic common algorithms fail in highdimensional spaces from tackling problems such as large number of groups, and overlapping. Most …
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Coping With New Challengens for Density-Based Clustering
… patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is …
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Efficient density-based methods for knowledge discovery in databases
… data mining task. Major data mining tasks are clustering and classification. Density-based approaches have proven to be very effective for many data mining methods. However, the good effectiveness often comes at the cost of a high runtime complexity. This thesis presents new efficient …
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Identification of gene expression changes in human cancer using bioinformatic approaches
… analysis of expression data, scalable subspace clustering, and curation of experimental gene regulation data from the published literature. I found that combining results from different expression platforms increases reliability of coexpression predictions. However, I also observed that …
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Similarity modeling for machine learning
… from the perspective of manifold learning and subspace learning. Our sparse similarity modeling methods learn sparse similarity and consequently generate a sparse graph over the data. The generated sparse graph leads to superior performance in clustering and semi-supervised learning, compared …
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Finding patterns in features and observations : new machine learning models with applications in computational criminology, marketing, and medicine
… a couple of seed crimes. The second method is a subspace clustering with cluster-specific feature selection, which is supervised when learning similarity graphs in order to reduce computation. Both methods we propose achieved promising results on a decade's worth of crime pattern data collected …