{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:PWZM7BYP2YOKDIC2Y4NL4B2Y3J","short_pith_number":"pith:PWZM7BYP","schema_version":"1.0","canonical_sha256":"7db2cf870fd61ca1a05ac71abe0758da6224ea7778b2dbd8189d99c8bfbe9326","source":{"kind":"arxiv","id":"1606.04366","version":3},"attestation_state":"computed","paper":{"title":"Recursive nonlinear-system identification using latent variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Dave Zachariah, Per Mattsson, Petre Stoica","submitted_at":"2016-06-14T13:46:21Z","abstract_excerpt":"In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear syst"},"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":"1606.04366","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-14T13:46:21Z","cross_cats_sorted":[],"title_canon_sha256":"6a28a40fbdb91338e9efbb67885408cbedd51b5d4137fefeebe8bfb2e5cd4132","abstract_canon_sha256":"685e1083b01deca22793199458e0b03cf83782fb72754da50b22326e22658123"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:00.245730Z","signature_b64":"+qHBfI11EaX8Bwu9jonSz6uRV802ZjKfEgWIwpNK/Xy7188FHqcD0z2Xxk5V7v8PgS/EBAbu3HPIN5KXNoi9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7db2cf870fd61ca1a05ac71abe0758da6224ea7778b2dbd8189d99c8bfbe9326","last_reissued_at":"2026-05-18T00:15:00.244942Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:00.244942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recursive nonlinear-system identification using latent variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Dave Zachariah, Per Mattsson, Petre Stoica","submitted_at":"2016-06-14T13:46:21Z","abstract_excerpt":"In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear syst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.04366","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":"1606.04366","created_at":"2026-05-18T00:15:00.245080+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.04366v3","created_at":"2026-05-18T00:15:00.245080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.04366","created_at":"2026-05-18T00:15:00.245080+00:00"},{"alias_kind":"pith_short_12","alias_value":"PWZM7BYP2YOK","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"PWZM7BYP2YOKDIC2","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"PWZM7BYP","created_at":"2026-05-18T12:30:39.010887+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/PWZM7BYP2YOKDIC2Y4NL4B2Y3J","json":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J.json","graph_json":"https://pith.science/api/pith-number/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/graph.json","events_json":"https://pith.science/api/pith-number/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/events.json","paper":"https://pith.science/paper/PWZM7BYP"},"agent_actions":{"view_html":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J","download_json":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J.json","view_paper":"https://pith.science/paper/PWZM7BYP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.04366&json=true","fetch_graph":"https://pith.science/api/pith-number/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/graph.json","fetch_events":"https://pith.science/api/pith-number/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/action/storage_attestation","attest_author":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/action/author_attestation","sign_citation":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/action/citation_signature","submit_replication":"https://pith.science/pith/PWZM7BYP2YOKDIC2Y4NL4B2Y3J/action/replication_record"}},"created_at":"2026-05-18T00:15:00.245080+00:00","updated_at":"2026-05-18T00:15:00.245080+00:00"}