Pages that link to "Item:Q351503"

The following pages link to A mathematical introduction to compressive sensing (Q351503):

Displaying 50 items.

• Efficient dictionary learning with sparseness-enforcing projections (Q1799978) (← links)
• Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling (Q1979911) (← links)
• The recovery of ridge functions on the hypercube suffers from the curse of dimensionality (Q1996887) (← links)
• A deterministic sparse FFT for functions with structured Fourier sparsity (Q2000485) (← links)
• Sparse approximate reconstruction decomposed by two optimization problems (Q2003320) (← links)
• Measurement matrix optimization via mutual coherence minimization for compressively sensed signals reconstruction (Q2004244) (← links)
• Compressed solving: a numerical approximation technique for elliptic PDEs based on compressed sensing (Q2006439) (← links)
• Single snapshot DOA estimation by minimizing the fraction function in sparse recovery (Q2007122) (← links)
• Learning general sparse additive models from point queries in high dimensions (Q2007617) (← links)
• An algebraic perspective on integer sparse recovery (Q2007643) (← links)
• Computing the spark: mixed-integer programming for the (vector) matroid girth problem (Q2007824) (← links)
• Complexity and applications of the homotopy principle for uniformly constrained sparse minimization (Q2019907) (← links)
• Stochastic greedy algorithms for multiple measurement vectors (Q2028927) (← links)
• Sparse random matrices have simple spectrum (Q2028938) (← links)
• Sparse harmonic transforms: a new class of sublinear-time algorithms for learning functions of many variables (Q2031058) (← links)
• An introduction to compressed sensing (Q2035070) (← links)
• Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs (Q2036421) (← links)
• Low-rank matrix recovery via regularized nuclear norm minimization (Q2036488) (← links)
• Random sampling and reconstruction of concentrated signals in a reproducing kernel space (Q2036501) (← links)
• Sparse harmonic transforms. II: Best \(s\)-term approximation guarantees for bounded orthonormal product bases in sublinear-time (Q2038427) (← links)
• Data-driven algorithm selection and tuning in optimization and signal processing (Q2043447) (← links)
• A Laplacian approach to \(\ell_1\)-norm minimization (Q2044482) (← links)
• On the optimal constants in the two-sided Stechkin inequalities (Q2045837) (← links)
• Donoho-Logan large sieve principles for modulation and polyanalytic Fock spaces (Q2046116) (← links)
• Tensor theta norms and low rank recovery (Q2048814) (← links)
• Gelfand numbers of embeddings of Schatten classes (Q2049959) (← links)
• Sparse recovery using the discrete cosine transform (Q2050708) (← links)
• Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems (Q2052391) (← links)
• Nonuniqueness of solutions of a class of \(\ell_0\)-minimization problems (Q2059197) (← links)
• Optimal fast Johnson-Lindenstrauss embeddings for large data sets (Q2059797) (← links)
• Tight bounds on the mutual coherence of sensing matrices for Wigner d-functions on regular grids (Q2059808) (← links)
• Dimensionality reduction for \(k\)-distance applied to persistent homology (Q2063202) (← links)
• Image multiplicative denoising using adaptive Euler's elastica as the regularization (Q2067301) (← links)
• A deterministic algorithm for constructing multiple rank-1 lattices of near-optimal size (Q2070286) (← links)
• Robust non-parametric regression via incoherent subspace projections (Q2071515) (← links)
• A simple recovery framework for signals with time-varying sparse support (Q2072588) (← links)
• Sparse Fourier transforms on rank-1 lattices for the rapid and low-memory approximation of functions of many variables (Q2073139) (← links)
• Sparsest piecewise-linear regression of one-dimensional data (Q2074905) (← links)
• Recovering sparse networks: basis adaptation and stability under extensions (Q2077695) (← links)
• Robust sparse recovery via a novel convex model (Q2079105) (← links)
• Partial gradient optimal thresholding algorithms for a class of sparse optimization problems (Q2079693) (← links)
• Sufficient conditions for the uniqueness of solution of the weighted norm minimization problem (Q2079999) (← links)
• The uniform sparse FFT with application to PDEs with random coefficients (Q2098298) (← links)
• Sensitivity of low-rank matrix recovery (Q2100520) (← links)
• Generalization bounds for sparse random feature expansions (Q2105118) (← links)
• The sparsity of LASSO-type minimizers (Q2105124) (← links)
• Hierarchical compressed sensing (Q2106467) (← links)
• Proof methods for robust low-rank matrix recovery (Q2106469) (← links)
• New challenges in covariance estimation: multiple structures and coarse quantization (Q2106471) (← links)
• Sparse deterministic and stochastic channels: identification of spreading functions and covariances (Q2106473) (← links)