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.
Results
Showing 1 to 8 of 8 for “"Sparsity Constraints"”.
-
A unified framework for identifiability analysis in bilinear inverse problems
… arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, properties of natural signals are exploited to solve BIPs. For example, subspace constraints or sparsity constraints are imposed to reduce the search space. These approaches have shown some …
-
Fast MRI with sparse sampling: models, algorithms, and applications
… advantages of incorporating both low-rank and sparsity models into the proposed formulation with a real-time cardiac imaging application. Second, we extend the joint low-rank and sparsity model to accelerate an important class of quantitative MRI problems, i.e., MR parameter mapping. We …
-
Exploiting biological pathways to infer temporal gene interaction models
… number of modes of interaction and imposing sparsity constraints to effectively limit the number of genes influencing each target gene makes the ill-posed problem of network inference tractable.
-
Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery
… arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, parsimonious structures of natural signals (e.g., subspace or sparsity) are exploited. However, there are few theoretical justifications for using such structures for BIPs. We consider two types …
-
Kernel and Manifold Framework for Magnetic Resonance Imaging
… to various inherent physical and physiological constraints. Such slow signal acquisition consequences in several limitations and challenges in imaging such as low spatiotemporal resolution, noise and image artifacts, inefficiency in imaging fast temporal dynamics, and patient discomfort. …
-
Change point detection for high dimensional data and valid inference for Bayesian linear models
… in high dimensional linear models under sparsity constraints. The idea is to use quasi Bayesian posteriors based on partial regression models to remove the effect of high dimensional nuisance variables and generate posterior samples of parameters for valid uncertainty quantification. We …
-
Multiframe Superresolution Techniques For Distributed Imaging Systems
… algorithm to efficiently incorporate sparsity as a form of prior knowledge. The prior knowledge that the object is sparse in some domain is incorporated in two ways: first we use the popular L1 norm as the regularization operator. Secondly we model wavelet coefficients of natural …
-
High dimensional information processing
… This can be viewed as a linear system with sparsity constraints corrupted by noise, where the objective is to estimate the sparsity pattern of β given the observation vector y and the measurement matrix X. First, we derive a non-asymptotic upper bound on the probability that a specific wrong …