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 “"Locally linear embedding"”.
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A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets
… to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inference system (ANFIS). The second method is a two stage system that uses various ANFIS with either single or …
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A Study of Dimensionality Reduction Techniques and its Analysis on Climate Data
… called the principle component space. These are linear techniques which can be expressed in the form B=TX where T is the transformation matrix that acts on the data matrix X to the reduced dimensionality representation B. Other linear techniques explored are Factor Analysis and Dictionary …
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms only provide the embedding results of training samples. There are many extensions of these approaches which try to solve the out-of-sample extension problem by …
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Data Reduction in Smart Grid
… performance improves. Recent studies using linear dimensionality reduction techniques indicate that high dimensional smart grid data may actually lie in a lower dimension. Taking into account the complexity of the smart grid, the process is inherently non-linear. Therefore, we critically …
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Exploring the dimensionality of speech using manifold learning and dimensionality reduction methods
… speech. However, if speech lies on a manifold nonlinearly embedded in high-dimensional space, as has been proposed in the past, classic linear dimensionality reduction methods would be unable to discover this embedding. In this dissertation a number of manifold learning, also referred to as …
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Realistic Motion Estimation Using Accelerometers
… learning and motion synthesis, respectively. Linear and nonlinear reduction techniques for data dimensionality are applied to search for the proper low dimensional representation of motion data. Two motion synthesis methods, interpolation and optimization, are compared using the 3D …