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 14 of 14 for “"Linear Embedding"”.
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Towards a Connection between Linear Embedding and the Poincaré Functional Equation.
<p>Several linear embeddings of the logistic equation, <em>x</em><sub><em>n</em>+1</sub>=<em>ax<sub>n</sub></em>(1-<em>x<sub>n</sub></em>) are considered, the goal being to establish a connection between linear embedding and the Poincaré Functional Equation. In particular, we consider linear …
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Parameter Estimation for a Modified Cable Model Using a Green's Function and Eigenvalue Perturbation.
… use the Green's Function to develop a Carleman linear embedding scheme which is used to estimate the effects of a nonlinear ion channel hot-spot on the tapered cylinder solution. Mathematica<sup>©</sup> was used to implement the Carleman embedding scheme.</p>
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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 multiple …
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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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Dimension Reduction on Measures of Impulsivity
… from a novel dimension technique known as Local Linear Embedding (LLE). LLE is an analysis of dimension reduction for nonlinear, high dimensional data. By computing neighborhood preserving embeddings, LLE aims to map newly constructed coordinates into a global coordinate structure of a lower …
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… 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 seeking an …
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Dimension reduction algorithms for near-optimal low-dimensional embeddings and compressive sensing
… Euclidean space, and the goal is to find a linear function from Rd into Rk , where k << d, such that the resulting embedding of the input pointset into k-dimensional Euclidean space has various desirable properties. We focus on two classes of theoretical results: -- First, we examine linear …
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The Linear Cutwidth and Cyclic Cutwidth of Complete n-Partite Graphs
… graphs, we strictly consider the linear embedding and cyclic embedding. The relationship between the linear cutwidth and the cyclic cutwidth is discussed and used throughout multiple proofs of different cases for the cyclic cutwidth. All the known cases for the linear and cyclic …
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Geometric Dimensionality Reduction
… Geometric Dimensionality Reduction, a non-linear data compression technique that utilizes low dimensional manifolds embedded in dimensional spaces to form composite contraction-and-projection maps. Geometric Dimensionality Reduction is predominantly demonstrated through a novel algorithm …
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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 …
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Building a robust clinical diagnosis support system for childhood cancer using data mining methods
… gene acting alone but are the result of complex linear and non-linear interactions among different types of microarray data. In this scenario, a single gene can have a small effect on disease but cannot be the major cause of the disease. For this reason there is a critical need to implement new …