{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QQHQJGLM7EKNKIS2GP3UC6OSE6","short_pith_number":"pith:QQHQJGLM","schema_version":"1.0","canonical_sha256":"840f04996cf914d5225a33f74179d2278c37fc82a37a516310dcfef9f5aac609","source":{"kind":"arxiv","id":"2006.16442","version":1},"attestation_state":"computed","paper":{"title":"Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jarvis Haupt, Sirisha Rambhatla, Xingguo Li","submitted_at":"2020-06-30T00:31:06Z","abstract_excerpt":"We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization of singular value decomposition (SVD) for tensors, reveals how the tensor dimensions (features) interact with each other. However, since the factors are a priori unknown, the corresponding optimization problems are inherently non-convex. The existing guaranteed algorithms which handle this non-convexity incur an irreducible error (bias), and only apply to cases where all factors have the same structure. To this end, w"},"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":"2006.16442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-30T00:31:06Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"76a6ceccdfa2aac378adc54a7cb4048f807554bc7c36a9fa3114d4df66a0433b","abstract_canon_sha256":"bcdda231f0864cbe2860e95feaef189c22e688bde575f4811cc9cabc13dc3b02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:14:55.061520Z","signature_b64":"Iue2JR1VYCQ9XPjgxj07U1GZZkO/ULtYYgLuzVudzNblRr3d2kQDGgZES0Hmsl/LMigheYyJJ1Gcq7V++LCxDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"840f04996cf914d5225a33f74179d2278c37fc82a37a516310dcfef9f5aac609","last_reissued_at":"2026-07-05T01:14:55.061179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:14:55.061179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jarvis Haupt, Sirisha Rambhatla, Xingguo Li","submitted_at":"2020-06-30T00:31:06Z","abstract_excerpt":"We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization of singular value decomposition (SVD) for tensors, reveals how the tensor dimensions (features) interact with each other. However, since the factors are a priori unknown, the corresponding optimization problems are inherently non-convex. The existing guaranteed algorithms which handle this non-convexity incur an irreducible error (bias), and only apply to cases where all factors have the same structure. To this end, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.16442","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.16442/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2006.16442","created_at":"2026-07-05T01:14:55.061234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.16442v1","created_at":"2026-07-05T01:14:55.061234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.16442","created_at":"2026-07-05T01:14:55.061234+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQHQJGLM7EKN","created_at":"2026-07-05T01:14:55.061234+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQHQJGLM7EKNKIS2","created_at":"2026-07-05T01:14:55.061234+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQHQJGLM","created_at":"2026-07-05T01:14:55.061234+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/QQHQJGLM7EKNKIS2GP3UC6OSE6","json":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6.json","graph_json":"https://pith.science/api/pith-number/QQHQJGLM7EKNKIS2GP3UC6OSE6/graph.json","events_json":"https://pith.science/api/pith-number/QQHQJGLM7EKNKIS2GP3UC6OSE6/events.json","paper":"https://pith.science/paper/QQHQJGLM"},"agent_actions":{"view_html":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6","download_json":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6.json","view_paper":"https://pith.science/paper/QQHQJGLM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.16442&json=true","fetch_graph":"https://pith.science/api/pith-number/QQHQJGLM7EKNKIS2GP3UC6OSE6/graph.json","fetch_events":"https://pith.science/api/pith-number/QQHQJGLM7EKNKIS2GP3UC6OSE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6/action/storage_attestation","attest_author":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6/action/author_attestation","sign_citation":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6/action/citation_signature","submit_replication":"https://pith.science/pith/QQHQJGLM7EKNKIS2GP3UC6OSE6/action/replication_record"}},"created_at":"2026-07-05T01:14:55.061234+00:00","updated_at":"2026-07-05T01:14:55.061234+00:00"}