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 6 of 6 for “"unsupervised feature selection"”.
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Unsupervised feature analysis for high dimensional big data
… can not be directly applied so we need to study unsupervised methods which could work well even without supervision. Feature analysis has been proven effective and important for many applications. Feature analysis is a broad research field, whose research topics includes but are not limited to …
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Localized Feature Selection For Unsupervised Learning
<p>Clustering is the unsupervised classification of data objects into different groups (clusters) such that objects in one group are similar together and dissimilar from another group. Feature selection for unsupervised learning is a technique that chooses the best feature subset for clustering. In …
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Nonparametric Modelling for Directional Data
… solution for usual statistical problems such as feature selection is provided. In this thesis, we present density estimators for the above three categories of directional observations under the framework of mixture models. Density estimation is a vital aspect in data analysis. It examines …
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Learning in Dynamic Data-Streams with a Scarcity of Labels
… The goal of this thesis is to evaluate unsupervised learning as the basis for online classification in dynamic data-streams with a scarcity of labels. To realise this goal, a novel stream clustering algorithm based on the collective behaviour of ants (Ant Colony Stream Clustering (ACSC)) …
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Recognition and investigation of temporal patterns in seismic wavefields using unsupervised learning techniques
… using manually defined training data or by unsupervised clustering and visualization. The latter allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. Besides classical earthquake seismology, investigations …
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PATIENT SIMILARITY NETWORKS-BASED METHODS FOR MULTIMODAL DATA INTEGRATION AND CLINICAL OUTCOME PREDICTION
… development of PSNs is highly dependent on the selection of patient similarity measures, we conduct a comprehensive review of the most commonly used and effective similarity metrics, encompassing multiple data types, including categorical, discrete, continuous, and binary. Next, considering the …