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 20 of 29 for “"Sparse representations"”.
-
Adaptive sparse representations and their applications
… model and its relationship to prior linear sparse models. Then, we propose novel problem formulations for learning square sparsifying transforms from data. The proposed algorithms for transform learning alternate between a sparse coding step and a transform update step, and are highly …
-
Boosting sparse representations for image retrieval
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
-
Information Retrieval with Dense and Sparse Representations
… the broader contextual relevance when using sparse retrieval. This thesis delves into improving both dense and sparse retrieval systems with advanced language models and training strategies. We first introduce DiffCSE, a difference-based contrastive learning framework for unsupervised …
-
Designing Sparse Representations for Efficient Planning in Uncertain Environments
… the relevant uncertainty, yet remain as sparse as possible, so that planning remains tractable. Higher model expressiveness improves plan quality but reduces computational efficiency in planning, whereas higher model sparsity improves efficiency at the cost of plan quality. Balancing …
-
Sparse Representations and Nonlinear Image Processing for Inverse Imaging Solutions
This work applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from a low-quality observed image. Sparse representations of images have drawn a …
-
Sparse modeling of high-dimensional data for learning and vision
Sparse representations account for most or all of the information of a signal by a linear combination of a few elementary signals called atoms, and have increasingly become recognized as providing high performance for applications as diverse as noise reduction, compression, inpainting, compressive …
-
Tomographic reconstruction with adaptive sparsifying transforms
… reconstructed images. Methods which exploit the sparse representations of tomographic images have long been known to improve the quality of reconstructions from low-dose data. Recent work has shown the promise of adaptive, rather than fixed, sparse representations. In particular, the synthesis …
-
Learning matrix and functional models in high-dimensions
… propose an asymptotically optimal procedure for sparse multi-task learning when the tasks share a joint support. We show consistency of the proposed method and derive rates of convergence. We next address the problem of learning meaningful representations of data. We propose a method for learning …
-
An exploration into the sparse representation of spectra
This thesis describes an exploration in achieving sparse representations of object, with special focus on spectral data. Given a database of objects one would like to know the actual aspects of each class that distinguish it from any other class in the database. We explore the hypothesis that …
-
Recovery of sparse signals and parameter perturbations from parameterized signal models
… signal has few nonzero elements. If a signal is sparse in a parameterized measurement model, the model parameters must be known to recover the signal. An example of this problem is the recovery of a signal that is a sum of a small number of sinusoids. Reconstruction of this signal requires …
-
Smooth Projections and Optimally Sparse Representation of Cartoon-like Cylindrical Solids
… two mathematical structures: projections and sparse representations. Projections are self-adjoint and idempotent linear operators that can be used to decompose a function space into smaller components thus making it easier to analyze. First, we extend the construction of smooth orthogonal …
-
Selection of Step Size for Total Variation Minimization in CT
… literature, it hasbeen shown that signals with sparse representations in an orthonormal basis may be reconstructed via l1-minimization. Furthermore, if an image can be approximately modeled to be piecewise constant, then its gradient is sparse. The application of l1-minimization to a sparse …
-
Voltage Regulation of Smart Grids using Machine Learning Tools
… the support vector machines, control rules with sparse representations are obtained which decrease the communication between the operator and the inverters. The designed control rules are tested under different grid conditions and compared with other reactive power control schemes. The results …
-
Greedy Algorithms In Approximation Theory and Compressed Sensing
<p>Sparse representations of a function is a very powerful tool to analyze and approximate the function. It has been utilized in many applications such as signal/image processing and numerical computation. One of the fundamental questions in this consideration is how to construct good methods …
-
Image super-resolution via sparse representation
… to single-image super-resolution (SR), based on sparse signal recovery. Research on image statistics suggests that image patches can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse …
-
DIRECTIONAL MULTISCALE ANALYSIS USING SHEARLET THEORY AND APPLICATIONS
… analysis as a general framework to provide sparse representations of multidimensional data. This construction was motivated by the need to provide more efficient algorithms for data analysis and processing, overcoming the limitations of traditional multiscale methods. Particularly, shearlets …
-
Directional image representations using nonseparable lifting
Sparse representations of visual information are essential for many image processing tasks. Because of the nonstationary geometric structure of natural images, representations derived from one-dimensional tensor products or compact frequency support will be suboptimal. Therefore, there is strong …
Page 1 of 2