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 16 of 16 for “"Dimensionality reduction methods"”.
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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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Exploring the dimensionality of speech using manifold learning and dimensionality reduction methods
… subset of all producible sounds. A number of dimensionality reduction methods capable of discovering such underlying structure have previously been applied to speech. However, if speech lies on a manifold nonlinearly embedded in high-dimensional space, as has been proposed in the past, classic …
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Efficient similarity search in high-dimensional data spaces
… search also tends to be unsatisfactory when the dimensionality is high. This is due to the poor index performance caused by the dimensionality curse. Dimensionality reduction using the Singular Value Decomposition method is the approach adopted in this study to deal with high-dimensional data. …
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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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Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
… with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). This work also includes two …
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Knowledge-Guided Machine Learning for Single-Cell Regulatory Genomics
… making it difficult for existing methods to reliably infer TF activity and gene-regulatory networks. This defense presents a set of knowledge-guided machine-learning approaches that embed prior biological evidence into modern analytical models to improve regulatory inference from …
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A Statistical Approach to Facial Identification
… thesis describes the development of statistical methods for facial identification. The objective is to provide a technique which can provide answers based on probabilities to the question of whether two images of a face are from the same person or whether there could be two different people whose …
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Robust and interpretable high-dimensional machine learning for predictive cancer medicine
… data is challenging and often relies upon dimensionality reduction techniques. These methods reveal structure within data, potentially exposing meaningful biological patterns. In predictive cancer medicine, it is common to employ linear dimensionality reduction methods due to their inherent …
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Design of wide-area electric transmission networks under uncertainty : methods for dimensionality reduction
… the strategic planning problem and develops dimensionality reduction methods to solve this otherwise computationally intractable problem. This work demonstrates three complementary methods to tractably solve multi-stage stochastic transmission network expansion planning. The first method, the …
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Similarity Metrics for Biological Data: Algorithmic developments for high-dimensional datasets
Advances in experimental methods in biology have allowed researchers to gain an unprecedentedly high-resolution view of the molecular processes within cells, using so-called single-cell technologies. Every cell in the sample can be individually profiled — the amount of each type of protein or …
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Advanced Data Analysis Of In-Situ Bioremediation Site Data Using Dimensionality Reduction Techniques
… be performed. Computer science algorithms for dimensionality reduction are common in research and certain industries that use “big data”, however these techniques have yet to be adapted for environmental industry needs or performance monitoring of ISB applications in particular. In this study a …
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Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data
… themachine learning research community. The high dimensionality ofthe data is one of the integral features that has to be considered whenbuilding predicting models. A single sample of the data is expressedby thousands of gene expressions compared to the benchmark imagesand texts that only have a …
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Principal component analysis and classification of discrete and mixed feature datasets using Gaussian copula
… mixed features are quite limited, as well as the dimensionality reduction methods and classification methods. In this thesis, we proposed a model based on the Gaussian copula to perform dimensionality reduction and classification for datasets with purely discrete or mixed features. In Chapter 3, …
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Genetic association of high-dimensional traits
… their computational challenges in GWAS, the true dimensionality of very high-dimensional phenotypes is often unknown and lies hidden in high-dimen- sional space. Retaining maximum power for association studies of such phenotype data relies on using an appropriate phenotype representation. I …