Pages that link to "Item:Q3097486"

The following pages link to New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property (Q3097486):

Displaying 37 items.

• Classification Scheme for Binary Data with Extensions (Q3296178) (← links)
• Tighter Fourier Transform Lower Bounds (Q3448770) (← links)
• A Novel Compressed Sensing Scheme for Photoacoustic Tomography (Q3452594) (← links)
• Compressive Sensing with Redundant Dictionaries and Structured Measurements (Q3455237) (← links)
• A Survey of Compressed Sensing (Q3460827) (← links)
• The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Compressed Sensing (Q3460832) (← links)
• Quantization and Compressive Sensing (Q3460834) (← links)
• Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication (Q3460837) (← links)
• Dimensionality-reduced subspace clustering (Q4603708) (← links)
• Simple Classification using Binary Data (Q4614095) (← links)
• (Q4638108) (← links)
• (Q4969055) (← links)
• Performance of Johnson--Lindenstrauss Transform for $k$-Means and $k$-Medians Clustering (Q5067443) (← links)
• Fast deflation sparse principal component analysis via subspace projections (Q5107781) (← links)
• An investigation of Newton-Sketch and subsampled Newton methods (Q5135249) (← links)
• On Using Toeplitz and Circulant Matrices for Johnson-Lindenstrauss Transforms (Q5136251) (← links)
• Persistent homology for low-complexity models (Q5160778) (← links)
• Minimization of the difference of Nuclear and Frobenius norms for noisy low rank matrix recovery (Q5221434) (← links)
• On the Atomic Decomposition of Coorbit Spaces with Non-integrable Kernel (Q5230193) (← links)
• Rendition: Reclaiming What a Black Box Takes Away (Q5236624) (← links)
• Simple Analyses of the Sparse Johnson-Lindenstrauss Transform. (Q5240430) (← links)
• The Restricted Isometry Property of Subsampled Fourier Matrices (Q5278293) (← links)
• Sparsity lower bounds for dimensionality reducing maps (Q5495780) (← links)
• (Q5743469) (← links)
• Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares (Q5857850) (← links)
• Johnson–Lindenstrauss Embeddings with Kronecker Structure (Q5885797) (← links)
• Randomized numerical linear algebra: Foundations and algorithms (Q5887823) (← links)
• Compressed data separation via unconstrained l1-split analysis (Q5889890) (← links)
• Dictionary-sparse recovery via thresholding-based algorithms (Q5964229) (← links)
• Lower bounds on the low-distortion embedding dimension of submanifolds of \(\mathbb{R}^n\) (Q6038821) (← links)
• \( \varepsilon \)-isometric dimension reduction for incompressible subsets of \(\ell_p\) (Q6145674) (← links)
• On fast Johnson-Lindenstrauss embeddings of compact submanifolds of \(\mathbb{R}^N\) with boundary (Q6151027) (← links)
• Fast Metric Embedding into the Hamming Cube (Q6154196) (← links)
• Enhanced total variation minimization for stable image reconstruction (Q6162738) (← links)
• Compressed data separation via \(\ell_q\)-split analysis with \(\ell_\infty\)-constraint (Q6596308) (← links)
• Convergence on thresholding-based algorithms for dictionary-sparse recovery (Q6669595) (← links)
• Applied harmonic analysis and data science. Abstracts from the workshop held April 21--26, 2024 (Q6671618) (← links)