{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JGIDKLM23FZXD6EVLGHEQUTIAA","short_pith_number":"pith:JGIDKLM2","schema_version":"1.0","canonical_sha256":"4990352d9ad97371f895598e485268002cfa911f32f25d94147bd870643728a3","source":{"kind":"arxiv","id":"1812.06888","version":1},"attestation_state":"computed","paper":{"title":"Tensor Ensemble Learning for Multidimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ahmad Moniri, Danilo P. Mandic, Ilia Kisil","submitted_at":"2018-12-17T16:50:03Z","abstract_excerpt":"In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance. The TEL "},"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":"1812.06888","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-12-17T16:50:03Z","cross_cats_sorted":[],"title_canon_sha256":"aa4a81541cdc08351f0a8b0096ce1cc5a286dd83d291e0b07085f4fdcc4d2a1d","abstract_canon_sha256":"ea941caab48b0e79053f83b98dc67c867222e9af03676e0554c7f230e45c890e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:09.140469Z","signature_b64":"V9MHZFKRdByKGEMYDRyxFGQqAhPYEJU+lGBiPCqUKBGEbr52pWdGBqt3TQs8u+ewofaTjSHhP4hEw8Z63wcOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4990352d9ad97371f895598e485268002cfa911f32f25d94147bd870643728a3","last_reissued_at":"2026-05-17T23:58:09.139812Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:09.139812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor Ensemble Learning for Multidimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ahmad Moniri, Danilo P. Mandic, Ilia Kisil","submitted_at":"2018-12-17T16:50:03Z","abstract_excerpt":"In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance. The TEL "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06888","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":"1812.06888","created_at":"2026-05-17T23:58:09.139900+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.06888v1","created_at":"2026-05-17T23:58:09.139900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06888","created_at":"2026-05-17T23:58:09.139900+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGIDKLM23FZX","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGIDKLM23FZXD6EV","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGIDKLM2","created_at":"2026-05-18T12:32:31.084164+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/JGIDKLM23FZXD6EVLGHEQUTIAA","json":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA.json","graph_json":"https://pith.science/api/pith-number/JGIDKLM23FZXD6EVLGHEQUTIAA/graph.json","events_json":"https://pith.science/api/pith-number/JGIDKLM23FZXD6EVLGHEQUTIAA/events.json","paper":"https://pith.science/paper/JGIDKLM2"},"agent_actions":{"view_html":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA","download_json":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA.json","view_paper":"https://pith.science/paper/JGIDKLM2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.06888&json=true","fetch_graph":"https://pith.science/api/pith-number/JGIDKLM23FZXD6EVLGHEQUTIAA/graph.json","fetch_events":"https://pith.science/api/pith-number/JGIDKLM23FZXD6EVLGHEQUTIAA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA/action/storage_attestation","attest_author":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA/action/author_attestation","sign_citation":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA/action/citation_signature","submit_replication":"https://pith.science/pith/JGIDKLM23FZXD6EVLGHEQUTIAA/action/replication_record"}},"created_at":"2026-05-17T23:58:09.139900+00:00","updated_at":"2026-05-17T23:58:09.139900+00:00"}