{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:45P4WUI6PUCWCPJ2AEZ6STEHZI","short_pith_number":"pith:45P4WUI6","schema_version":"1.0","canonical_sha256":"e75fcb511e7d05613d3a0133e94c87ca082c3a757abb5a3a3debf61490064baf","source":{"kind":"arxiv","id":"1312.3968","version":2},"attestation_state":"computed","paper":{"title":"Generalized Approximate Message Passing for Cosparse Analysis Compressive Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Mark Borgerding, Philip Schniter, Sundeep Rangan","submitted_at":"2013-12-13T21:51:20Z","abstract_excerpt":"In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given linear transform of the signal is cosparse, i.e., has sufficiently many zeros. We propose a novel approach to cosparse analysis CS based on the generalized approximate message passing (GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works with a wide range of analysis operators and regularizers. In addition, we propose a novel $\\ell_0$-like soft-thresholder based on MMSE denoising for a spike"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1312.3968","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2013-12-13T21:51:20Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"ba9a7175e3b691bd863ba2adea03fc8cd3d4158f3601febf214c37b2dbf16407","abstract_canon_sha256":"1290b34ee329b8ea54445ab32a8812861453be5b67ce3da5d2e0332be98fa065"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:39:48.521977Z","signature_b64":"Hvmgj046cinXerEUwg5ce+yN9tEjEwXl7RNirZdobSa1hCrvZSRZTOB1u85qAKw6NsFZYV5JOTakNbgJOLsQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e75fcb511e7d05613d3a0133e94c87ca082c3a757abb5a3a3debf61490064baf","last_reissued_at":"2026-05-18T02:39:48.521510Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:39:48.521510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Approximate Message Passing for Cosparse Analysis Compressive Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Mark Borgerding, Philip Schniter, Sundeep Rangan","submitted_at":"2013-12-13T21:51:20Z","abstract_excerpt":"In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given linear transform of the signal is cosparse, i.e., has sufficiently many zeros. We propose a novel approach to cosparse analysis CS based on the generalized approximate message passing (GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works with a wide range of analysis operators and regularizers. In addition, we propose a novel $\\ell_0$-like soft-thresholder based on MMSE denoising for a spike"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.3968","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1312.3968","created_at":"2026-05-18T02:39:48.521586+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.3968v2","created_at":"2026-05-18T02:39:48.521586+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.3968","created_at":"2026-05-18T02:39:48.521586+00:00"},{"alias_kind":"pith_short_12","alias_value":"45P4WUI6PUCW","created_at":"2026-05-18T12:27:32.513160+00:00"},{"alias_kind":"pith_short_16","alias_value":"45P4WUI6PUCWCPJ2","created_at":"2026-05-18T12:27:32.513160+00:00"},{"alias_kind":"pith_short_8","alias_value":"45P4WUI6","created_at":"2026-05-18T12:27:32.513160+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI","json":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI.json","graph_json":"https://pith.science/api/pith-number/45P4WUI6PUCWCPJ2AEZ6STEHZI/graph.json","events_json":"https://pith.science/api/pith-number/45P4WUI6PUCWCPJ2AEZ6STEHZI/events.json","paper":"https://pith.science/paper/45P4WUI6"},"agent_actions":{"view_html":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI","download_json":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI.json","view_paper":"https://pith.science/paper/45P4WUI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.3968&json=true","fetch_graph":"https://pith.science/api/pith-number/45P4WUI6PUCWCPJ2AEZ6STEHZI/graph.json","fetch_events":"https://pith.science/api/pith-number/45P4WUI6PUCWCPJ2AEZ6STEHZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI/action/storage_attestation","attest_author":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI/action/author_attestation","sign_citation":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI/action/citation_signature","submit_replication":"https://pith.science/pith/45P4WUI6PUCWCPJ2AEZ6STEHZI/action/replication_record"}},"created_at":"2026-05-18T02:39:48.521586+00:00","updated_at":"2026-05-18T02:39:48.521586+00:00"}