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 230 for “"Dimensionality Reduction"”.
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Geometric Dimensionality Reduction
… has resulted in a surge of research into dimensionality reduction techniques that spans across numerous mathematical disciplines. In this thesis we establish Geometric Dimensionality Reduction, a non-linear data compression technique that utilizes low dimensional manifolds embedded in …
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Federated Linear Dimensionality Reduction
… Concretely, we focus primarily on linear dimensionality reduction and, in particular, on Principal Component Analysis (PCA) due to its pervasiveness, along with its ability to process unstructured data. The first advancement we introduce is a novel algorithm to perform streaming and …
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On Dimensionality Reduction of Data
… method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, …
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Autoencoder-based image dimensionality reduction methods
In this thesis, we study how images can be represented in a more compact way that still captures their most important features and preserves the similarities and dissimilarities between the images. These compact representations of images, also known as ‘image encodings’, allow us to identify …
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Graph Embedding and Nonlinear Dimensionality Reduction
… have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure. However, these methods often perform poorly when applied to data which does not lie exactly near a linear …
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Dimensionality reduction for k-means clustering
In this thesis we study dimensionality reduction techniques for approximate k-means clustering. Given a large dataset, we consider how to quickly compress to a smaller dataset (a sketch), such that solving the k-means clustering problem on the sketch will give an approximately optimal solution on …
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On Sequence Clustering and Supervised Dimensionality Reduction
… identically generated random sequences, and 2) dimensionality reduction for classification problems. </p> <p>For sequence clustering, the focus is on large sample performance of classical clustering algorithms, including the k-medoids algorithm and hierarchical agglomerative clustering (HAC) …
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Semi supervised weighted maximum variance dimensionality reduction
… in some scenarios. In those scenarios, the dimensionality reduction methods play a major role for extracting useful features. The two parameter weighted maximum variance (2P-WMV) is a generalized dimensionality reduction method of which principal component analysis (PCA) and maximum margin …
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Dimensionality reduction for sparse and structured matrices
Dimensionality reduction has become a critical tool for quickly solving massive matrix problems. Especially in modern data analysis and machine learning applications, an overabundance of data features or examples can make it impossible to apply standard algorithms efficiently. To address this …
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Dimensionality reduction in immunology : from viruses to cells
… and the computational sciences to "reduce the dimensionality" of such data in order to reveal novel biological relationships of relevance to vaccination and therapeutic strategies. Much of our work is concerned with HIV. 1. How can collective evolutionary constraints be inferred from viral …
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High-dimensional indexing methods utilizing clustering and dimensionality reduction
… in high-dimensional space due to the curse of dimensionality. This inefficiency is dealt in this study by Clustering and Singular Value Decomposition - CSVD with indexing, Persistent Main Memory - PMM index, and Stepwise Dimensionality Increasing - SDI-tree index. CSVD is an approximate nearest …
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Feature selection and dimensionality reduction for supervised data analysis
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016
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Applying a randomized nearest neighbors algorithm to dimensionality reduction
… algorithm in order to optimize an existing dimensionality reduction algorithm. In implementation I resolved details that were not considered in the design stage, and optimized the nearest neighbor system for use by the dimensionality reduction system. By using the new nearest neighbor system …
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Dimensionality Reduction, Feature Selection and Visualization of Biological Data
Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two …
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Clustering and dimensionality reduction for time-series service monitoring data
… to monitor their availability, therefore, high dimensionality, unlabeled data and changing data distribution are all prevalent. In this thesis, we efficiently address these three issues using the constructed service monitoring dataset. Higher dimensionality means higher computational cost to …
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Application of nonlinear dimensionality reduction to climate data for prediction
… methods are not suitable for characterising the dimensionality of the sea surface temperature in the tropical Pacific Ocean. Therefore they do not help to separate the oscillations by themselves. Instead, nonlinear methods of dimensionality reduction are proven to be better in defining a lower …
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Massive data visualization based on dimensionality reduction and projection error evaluation /
… data tasks. Comprehensive analysis of various dimensionality reduction techniques was performed while solving the dimensionality reduction problem. Analysis included various classic dimensionality methods and methods which are based on control point’s selection. The main results of the …
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