{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2008:DBNDRJAAFENUDG7I6G6RGCLC2A","short_pith_number":"pith:DBNDRJAA","schema_version":"1.0","canonical_sha256":"185a38a400291b419be8f1bd130962d01fe78bb4bcc0ed035008e04cf0497575","source":{"kind":"arxiv","id":"0809.4883","version":3},"attestation_state":"computed","paper":{"title":"Thresholded Basis Pursuit: An LP Algorithm for Achieving Optimal Support Recovery for Sparse and Approximately Sparse Signals from Noisy Random Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"M. Zhao, V. Saligrama","submitted_at":"2008-09-29T14:01:13Z","abstract_excerpt":"In this paper we present a linear programming solution for sign pattern recovery of a sparse signal from noisy random projections of the signal. We consider two types of noise models, input noise, where noise enters before the random projection; and output noise, where noise enters after the random projection. Sign pattern recovery involves the estimation of sign pattern of a sparse signal. Our idea is to pretend that no noise exists and solve the noiseless $\\ell_1$ problem, namely, $\\min \\|\\beta\\|_1 ~ s.t. ~ y=G \\beta$ and quantizing the resulting solution. We show that the quantized solution"},"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":"0809.4883","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2008-09-29T14:01:13Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"9cde8b760d58664a1ddd35c575d6d77a5c1c71d1408ab8e52162fbb61939a3ee","abstract_canon_sha256":"26a9b1b7bd3024de0459c1079c16fae2202dd59986246ee08142aeccc473e3fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:24:56.958657Z","signature_b64":"BL3PSANyWntrB4/pTgNsN0xfyFxaeHga3Yw3+3cpAypJQykpusKoLmGeEijRv0Y7FDV8EMR2UJl6/iJoedDQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"185a38a400291b419be8f1bd130962d01fe78bb4bcc0ed035008e04cf0497575","last_reissued_at":"2026-05-18T02:24:56.957881Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:24:56.957881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Thresholded Basis Pursuit: An LP Algorithm for Achieving Optimal Support Recovery for Sparse and Approximately Sparse Signals from Noisy Random Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"M. Zhao, V. Saligrama","submitted_at":"2008-09-29T14:01:13Z","abstract_excerpt":"In this paper we present a linear programming solution for sign pattern recovery of a sparse signal from noisy random projections of the signal. We consider two types of noise models, input noise, where noise enters before the random projection; and output noise, where noise enters after the random projection. Sign pattern recovery involves the estimation of sign pattern of a sparse signal. Our idea is to pretend that no noise exists and solve the noiseless $\\ell_1$ problem, namely, $\\min \\|\\beta\\|_1 ~ s.t. ~ y=G \\beta$ and quantizing the resulting solution. We show that the quantized solution"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0809.4883","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"0809.4883","created_at":"2026-05-18T02:24:56.958013+00:00"},{"alias_kind":"arxiv_version","alias_value":"0809.4883v3","created_at":"2026-05-18T02:24:56.958013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0809.4883","created_at":"2026-05-18T02:24:56.958013+00:00"},{"alias_kind":"pith_short_12","alias_value":"DBNDRJAAFENU","created_at":"2026-05-18T12:25:57.157939+00:00"},{"alias_kind":"pith_short_16","alias_value":"DBNDRJAAFENUDG7I","created_at":"2026-05-18T12:25:57.157939+00:00"},{"alias_kind":"pith_short_8","alias_value":"DBNDRJAA","created_at":"2026-05-18T12:25:57.157939+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/DBNDRJAAFENUDG7I6G6RGCLC2A","json":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A.json","graph_json":"https://pith.science/api/pith-number/DBNDRJAAFENUDG7I6G6RGCLC2A/graph.json","events_json":"https://pith.science/api/pith-number/DBNDRJAAFENUDG7I6G6RGCLC2A/events.json","paper":"https://pith.science/paper/DBNDRJAA"},"agent_actions":{"view_html":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A","download_json":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A.json","view_paper":"https://pith.science/paper/DBNDRJAA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0809.4883&json=true","fetch_graph":"https://pith.science/api/pith-number/DBNDRJAAFENUDG7I6G6RGCLC2A/graph.json","fetch_events":"https://pith.science/api/pith-number/DBNDRJAAFENUDG7I6G6RGCLC2A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A/action/storage_attestation","attest_author":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A/action/author_attestation","sign_citation":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A/action/citation_signature","submit_replication":"https://pith.science/pith/DBNDRJAAFENUDG7I6G6RGCLC2A/action/replication_record"}},"created_at":"2026-05-18T02:24:56.958013+00:00","updated_at":"2026-05-18T02:24:56.958013+00:00"}