{"paper":{"title":"Learning-based Compressive Subsampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Baran G\\\"ozc\\\"u, Ilija Bogunovic, Jonathan Scarlett, Luca Baldassarre, Volkan Cevher, Yen-Huan Li","submitted_at":"2015-10-21T10:03:45Z","abstract_excerpt":"The problem of recovering a structured signal $\\mathbf{x} \\in \\mathbb{C}^p$ from a set of dimensionality-reduced linear measurements $\\mathbf{b} = \\mathbf {A}\\mathbf {x}$ arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix $\\mathbf{A} \\in \\mathbb{C}^{n \\times p}$ is often of the form $\\mathbf{A} = \\mathbf{P}_{\\Omega}\\boldsymbol{\\Psi}$ for some orthonormal basis matrix $\\boldsymbol{\\Psi}\\in \\mathbb{C}^{p \\times p}"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.06188","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}