{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XTJZ44EYLEBHK2XCMEMM4KTUD7","short_pith_number":"pith:XTJZ44EY","schema_version":"1.0","canonical_sha256":"bcd39e70985902756ae26118ce2a741ff8f4f9c882b3e089dd51d8c91cdcef71","source":{"kind":"arxiv","id":"1704.07790","version":1},"attestation_state":"computed","paper":{"title":"FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoyi Xiong, Jiang Bian, Wei Cheng, Wenqing Hu, Zhishan Guo","submitted_at":"2017-04-25T17:11:57Z","abstract_excerpt":"Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data is widely-used for early detection of diseases. Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e.g., covariance matrix), and the \"linear inseparability\" of EHR data. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictions in an ensemble way. Specifically, FWDA first surrogates the distribution of inverse covariance matrices using a Wishart distribution estimated"},"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":"1704.07790","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-25T17:11:57Z","cross_cats_sorted":[],"title_canon_sha256":"60d962657f8792d13e96ad6527ff56afd19580298d7b87f247c5606455d679b2","abstract_canon_sha256":"eb92242be8e87155fde94f0c74fcaee401c4001ea0a695f159557a69c36fa4d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:36.917963Z","signature_b64":"QBwwJFNgLj3B7bNi/fJbyiMTMjLKMA5etzCtic8Hh2UZd13+oUqJiw2iQufovOd1MjNNBBPp4tD4FdKgJgZVCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bcd39e70985902756ae26118ce2a741ff8f4f9c882b3e089dd51d8c91cdcef71","last_reissued_at":"2026-05-18T00:45:36.917182Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:36.917182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoyi Xiong, Jiang Bian, Wei Cheng, Wenqing Hu, Zhishan Guo","submitted_at":"2017-04-25T17:11:57Z","abstract_excerpt":"Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data is widely-used for early detection of diseases. Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e.g., covariance matrix), and the \"linear inseparability\" of EHR data. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictions in an ensemble way. Specifically, FWDA first surrogates the distribution of inverse covariance matrices using a Wishart distribution estimated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.07790","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":"1704.07790","created_at":"2026-05-18T00:45:36.917530+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.07790v1","created_at":"2026-05-18T00:45:36.917530+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.07790","created_at":"2026-05-18T00:45:36.917530+00:00"},{"alias_kind":"pith_short_12","alias_value":"XTJZ44EYLEBH","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XTJZ44EYLEBHK2XC","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XTJZ44EY","created_at":"2026-05-18T12:31:56.362134+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/XTJZ44EYLEBHK2XCMEMM4KTUD7","json":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7.json","graph_json":"https://pith.science/api/pith-number/XTJZ44EYLEBHK2XCMEMM4KTUD7/graph.json","events_json":"https://pith.science/api/pith-number/XTJZ44EYLEBHK2XCMEMM4KTUD7/events.json","paper":"https://pith.science/paper/XTJZ44EY"},"agent_actions":{"view_html":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7","download_json":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7.json","view_paper":"https://pith.science/paper/XTJZ44EY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.07790&json=true","fetch_graph":"https://pith.science/api/pith-number/XTJZ44EYLEBHK2XCMEMM4KTUD7/graph.json","fetch_events":"https://pith.science/api/pith-number/XTJZ44EYLEBHK2XCMEMM4KTUD7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7/action/storage_attestation","attest_author":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7/action/author_attestation","sign_citation":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7/action/citation_signature","submit_replication":"https://pith.science/pith/XTJZ44EYLEBHK2XCMEMM4KTUD7/action/replication_record"}},"created_at":"2026-05-18T00:45:36.917530+00:00","updated_at":"2026-05-18T00:45:36.917530+00:00"}