Nonlinear least squares in \(\mathbb R^{N}\)

DOI10.1007/S10440-008-9398-9zbMath1175.94042OpenAlexW2098798840MaRDI QIDQ1038734

Kourosh Zaringhalam, Akram Al-Droubi

Publication date: 20 November 2009

Published in: Acta Applicandae Mathematicae (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10440-008-9398-9

zbMATH Keywords

image processingsignal processingcompressed sensingsignal modelingcompressed sampling

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (2)

Reduced row echelon form and non-linear approximation for subspace segmentation and high-dimensional data clustering ⋮ Similarity matrix framework for data from union of subspaces

Cites Work

• Optimal non-linear models for sparsity and sampling
• A unified algebraic approach to 2-D and 3-D motion segmentation and estimation
• Deterministic constructions of compressed sensing matrices
• On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them
• Quantitative robust uncertainty principles and optimally sparse decompositions
• Nonuniform Sampling and Reconstruction in Shift-Invariant Spaces
• Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Sparse representations in unions of bases
• Greed is Good: Algorithmic Results for Sparse Approximation
• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• Compressed Sensing and Redundant Dictionaries
• Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
• A Theory for Sampling Signals From a Union of Subspaces
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Sampling and reconstruction of signals with finite rate of innovation in the presence of noise
• Compressed sensing

This page was built for publication: Nonlinear least squares in \(\mathbb R^{N}\)