{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:GREF3HYJXUMBBLY6B3C45X5OLF","short_pith_number":"pith:GREF3HYJ","schema_version":"1.0","canonical_sha256":"34485d9f09bd1810af1e0ec5cedfae59780e892763189e4797f899c966b05212","source":{"kind":"arxiv","id":"1512.03224","version":1},"attestation_state":"computed","paper":{"title":"Spectral Compressed Sensing via CANDECOMP/PARAFAC Decomposition of Incomplete Tensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NA","authors_text":"Hongbin Li, Jun Fang, Linxiao Yang","submitted_at":"2015-12-10T12:04:56Z","abstract_excerpt":"We consider the line spectral estimation problem which aims to recover a mixture of complex sinusoids from a small number of randomly observed time domain samples. Compressed sensing methods formulates line spectral estimation as a sparse signal recovery problem by discretizing the continuous frequency parameter space into a finite set of grid points. Discretization, however, inevitably incurs errors and leads to deteriorated estimation performance. In this paper, we propose a new method which leverages recent advances in tensor decomposition. Specifically, we organize the observed data into a"},"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":"1512.03224","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2015-12-10T12:04:56Z","cross_cats_sorted":[],"title_canon_sha256":"d9c65122cb59d82eb9878d988ff22744312539d75c2cf76bf117c1be68de8d9c","abstract_canon_sha256":"e8ce060def2f4f6e667c042977dd6c0ea895d54817ef6a805b09c03499fb7d8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:37.604121Z","signature_b64":"9TyBwmwkEQiog+2A0ajeDKlMlE4pouYD9O+TgYbni6of501b9m2PikIhaiU8tNBo7E7kaLIDmyI8gJaIPKjcBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34485d9f09bd1810af1e0ec5cedfae59780e892763189e4797f899c966b05212","last_reissued_at":"2026-05-18T01:24:37.603686Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:37.603686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral Compressed Sensing via CANDECOMP/PARAFAC Decomposition of Incomplete Tensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NA","authors_text":"Hongbin Li, Jun Fang, Linxiao Yang","submitted_at":"2015-12-10T12:04:56Z","abstract_excerpt":"We consider the line spectral estimation problem which aims to recover a mixture of complex sinusoids from a small number of randomly observed time domain samples. Compressed sensing methods formulates line spectral estimation as a sparse signal recovery problem by discretizing the continuous frequency parameter space into a finite set of grid points. Discretization, however, inevitably incurs errors and leads to deteriorated estimation performance. In this paper, we propose a new method which leverages recent advances in tensor decomposition. Specifically, we organize the observed data into a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03224","kind":"arxiv","version":1},"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":"1512.03224","created_at":"2026-05-18T01:24:37.603751+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.03224v1","created_at":"2026-05-18T01:24:37.603751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03224","created_at":"2026-05-18T01:24:37.603751+00:00"},{"alias_kind":"pith_short_12","alias_value":"GREF3HYJXUMB","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"GREF3HYJXUMBBLY6","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"GREF3HYJ","created_at":"2026-05-18T12:29:22.688609+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/GREF3HYJXUMBBLY6B3C45X5OLF","json":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF.json","graph_json":"https://pith.science/api/pith-number/GREF3HYJXUMBBLY6B3C45X5OLF/graph.json","events_json":"https://pith.science/api/pith-number/GREF3HYJXUMBBLY6B3C45X5OLF/events.json","paper":"https://pith.science/paper/GREF3HYJ"},"agent_actions":{"view_html":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF","download_json":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF.json","view_paper":"https://pith.science/paper/GREF3HYJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.03224&json=true","fetch_graph":"https://pith.science/api/pith-number/GREF3HYJXUMBBLY6B3C45X5OLF/graph.json","fetch_events":"https://pith.science/api/pith-number/GREF3HYJXUMBBLY6B3C45X5OLF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF/action/storage_attestation","attest_author":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF/action/author_attestation","sign_citation":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF/action/citation_signature","submit_replication":"https://pith.science/pith/GREF3HYJXUMBBLY6B3C45X5OLF/action/replication_record"}},"created_at":"2026-05-18T01:24:37.603751+00:00","updated_at":"2026-05-18T01:24:37.603751+00:00"}