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 18 of 18 for “"low-dimensional subspace"”.
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Accelerating Bayesian Computation in Earth Remote Sensing Problems
… is computationally intractable given the high dimensionality of the problem. In many Bayesian inverse problems, however, there exists a low-dimensional likelihood-informed subspace that describes both optimal projections of the data and directions in parameter space that are most informed by …
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Sampling-based algorithms for dimension reduction
Can one compute a low-dimensional representation of any given data by looking only at its small sample, chosen cleverly on the fly? Motivated by the above question, we consider the problem of low-rank matrix approximation: given a matrix A..., one wants to compute a rank-k matrix (where k << min{m, …
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A hierarchical wavelet-based framework for pattern analysis and synthesis
… for different patterns, our framework provides a low-dimensional subspace classifier that is invariant to unknown pattern transformations as well as background clutter.
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Spectral Solution Method for Distributed Delay Stochastic Differential Equations
… stochastic delayed equations is constrained to a low-dimensional subspace. — The expression for the autocovariance is given particular attention. A recurring problem for stochastic delay equations is the description of their temporal structure. We show that the series expression for the …
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Characterizations of how neural networks learn
… study data where the labels depend on an unknown low-dimensional subspace of the input (i.e., the multi-index setting). We identify the “leap complexity”, which is a quantity that we argue characterizes how much data networks need in order to learn. Our analysis reveals a saddle-to-saddle dynamic …
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Dynamic model for space-time weather radar observation and nowcasting
… modeling spacetime radar observations: 1) high dimensionality due to the high-resolution radar measurements over a large area, 2) non-stationarity due to the storm motion, and 3) non-stationarity due to evolution (growth and decay). These difficulties are addressed in this research. To deal with …
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Graph-Based Acoustic Clustering and Classification
… the resulting frequency signature as a high-dimensional feature description of each data point. We then develop a graph-based approach for analyzing these signals, representing the data using a similarity graph. Following methods used successfully in image processing and problems on networks, …
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Estimation of Shared Functional Information Between Neural Areas
Recent technological advances now allow for detailed recordings of brain activity, capturing thousands of neurons over several days in animals engaged in complex behaviors. These datasets provide a unique opportunity to study how information is shared across neural areas during visually guided …
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Interference suppression and diversity for CDMA systems
… of data samples and the desired signal is in a low dimensional subspace. It is also demonstrated that the reduced-rank minimum variance receiver outperforms the full-rank minimum variance receiver. The probability density function of the output SNR of the full-rank and reduced-rank linear MMSE …
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A subspace approach to high-resolution magnetic resonance spectroscopic imaging
… of in vivo MRSI have been progressing more slowly than expected. The main reasons for this situation are the problems of long data acquisition time, poor spatial resolution and low signal-to-noise ratio (SNR) for this imaging modality. In the last four decades, significant efforts have been …
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Physics-informed neural surrogates for next-generation aerothermochemical modeling
… physical process in a wide range of reactive flow environments, spanning planetary entry and astrophysical flows to plasma-assisted combustion and flow control. State-to-state (StS) collisional–radiative (CR) models provide the highest level of physical fidelity by explicitly resolving the …
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Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction
… evaluations of the objectives and constraints allowed. Bayesian optimization (BO) has become a popular global optimization technique for solving problems governed by such expensive functions. BO iteratively updates a statistical model and uses it to quantify the expected benefits of evaluating a …
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Characterization of uncertainty in remotely-sensed precipitation estimates
… precipitation intensity dependence as well as a lower bias at higher intensities and in geographic locations where precipitation rates are generally higher. Next, a new stochastic method is developed to generate spatially intermittent precipitation replicates. These replicates constitute a prior …
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Stochastic Optimization For Multi-Agent Statistical Learning And Control
… gradient method (FSGD) with greedily constructed low-dimensional subspace projections based on matching pursuit. We establish that the proposed method yields a controllable trade-off between optimality and memory, and yields highly accurate parsimonious statistical models in practice. % Then, we …
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Markerless multiple-view human motion analysis using swarm optimisation and subspace learning
… particle swarm optimisation and charting, a subspace learning technique.In our first framework, we formulate, and perform, human motion tracking as a multi-dimensional non-linear optimisation problem, solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm. PSO …
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Scene Monitoring With A Forest Of Cooperative Sensors
… from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer …
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Experimental Investigations into the Fluid Dynamics and Forcing Underlying Cross-flow Turbine Operation
Within the wind and marine energy sectors, axial-flow (i.e., horizontal axis) turbines are a well-established and well-understood approach to converting the kinetic energy in a moving fluid to electricity. Recent cross-flow (i.e., vertical axis) turbine research has yielded substantial performance …
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Mechanisms underlying spatial navigation
… that the population activity evolved across a low-dimensional manifold that represented the virtual track. Noisy velocity inputs pushed the trajectory along the manifold, enabling the tracking of location. Using fixed-point analysis I found that the manifold was segmented by input-dependent …