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 “"Similarity matrix"”.
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LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS
… the nuclear norm to approximate the rank of a matrix. Despite the success of nuclear norm minimization in capturing the low intrinsic-dimensionality of data, the nuclear norm minimizes not only the rank, but also the variance of matrix and may not be a good approximation to the rank function in …
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Text readability and summarisation for non-native reading comprehension
… to extract features to measure the content similarity between the reading passage and the summary. In the second approach, we calculate a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, …
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APPLY DATA CLUSTERING TO GENE EXPRESSION DATA
… representation, the selection of gene datasets, Similarity Matrix Selection, the selection of clustering algorithm, and analysis tool. R language with the focus of Kmeans, fpc, hclust, and heatmap3 packages in R is used in this project as an analysis tool. Different clustering algorithms are used …
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Analogy-based software project effort estimation. Contributions to projects similarity measurement, attribute selection and attribute weighting algorithms for analogy-based effort estimation.
… based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the …
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Analogy-based software project effort estimation. Contributions to projects similarity measurement, attribute selection and attribute weighting algorithms for analogy-based effort estimation.
… based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the …
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Hybrid ConVIRT - enhancing medical image-text representation learning of vision language models
… feature alignment, it introduces a Fused Similarity Matrix (FSM), which serves as a more refined target, leveraging the learned capabilities of Med-CLIP and ConVIRT. Evaluation across four datasets (COVID, Tuberculosis, Pneumonia/TB Mix, and CheXpert) using linear probe and zero-shot …
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… eigenvectors of a data affinity (\ie, item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms only provide the …
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Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation
… can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network.
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Application of maximal information coefficient and affinity propagation to characterizing seismic time series associated with earthquakes
… series analysis (HCTSA) operation library. The similarity between each pair of features was represented by the measure of maximal information coefficient (MIC). MATLAB functions were implemented to compute the similarity matrix of the feature dataset generated by HCTSA. Affinity propagation (AP) …
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Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data
… on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is developed. The third topic involves a specific genetic pathway dataset in which the pathways interact with the environmental variables. We propose a semiparametric method to …
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Fine mapping studies of quantitative trait loci for baseline platelet count in mice and humans
… the novel development of an underlying genetic similarity matrix index to correct for population stratification. The results from this in-silico association were then correlated with expression array data from parental samples and five genes were prioritised for more detailed analysis by …
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Scalable centralized and distributed spectral clustering
… combination of eigenvectors of the normalized similarity matrix weighted with corresponding eigenvalues. This linear combination is then used to partition the dataset into meaningful clusters. Simulations on real datasets show that partitioning datasets based on such linear combinations of …
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Statistical methods for multi-omic data integration
… we build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian mixture models. A key contribution of our work is the observation that PSMs can be used to define probabilistically-motivated …
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Inferring Malware Detector Metrics in the Absence of Ground-Truth
… from the recovered detector metrics and the similarity matrix, and provide a method that allows for the selection of a heterogeneous selection of accurate detectors. We utilize this selection method to illustrate how detectors that are less well rated can be removed from the data set in a …
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Hash code learning for large scale similarity search
… distance calculations to approximate pairwise similarity. This graph can be used in various unsupervised hashing methods which require a similarity matrix. Current unsupervised image graph construction methods are dominated by those which utilize the manifold structure of images in the feature …
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Deep Graph Representation Learning and its Application on Graph Clustering
… used to learn the relatively global structural similarity. Then, the improved attention matrix was obtained by adding the relatively global structure similarity matrix to the traditional attention matrix. Finally, the graph representation was learned by the improved attention matrix. Graph …