{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:BNHNQNLW3YXUPRFPL5Q4DLRKQN","short_pith_number":"pith:BNHNQNLW","schema_version":"1.0","canonical_sha256":"0b4ed83576de2f47c4af5f61c1ae2a83556b6e7b5c6af17d9193e74a128f1f72","source":{"kind":"arxiv","id":"1711.05792","version":2},"attestation_state":"computed","paper":{"title":"Aggregated Wasserstein Metric and State Registration for Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jia Li, Jianbo Ye, Yukun Chen","submitted_at":"2017-11-12T22:43:22Z","abstract_excerpt":"We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time position follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of "},"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":"1711.05792","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-12T22:43:22Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"ca2977ceefdd12ce4c58848fbf3b1d50467d9a14eb618872fef522131440c051","abstract_canon_sha256":"2b5bf3f01c7ff3d6f5ede0ded3d7187b685a79cef586335a5427271f13a5a997"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:15.987217Z","signature_b64":"wNXNAa6B0JZtMTDlFR97fWu46/SZLjqk8DQv7AEqh5tOfLgise9w6crUhzf7aZUyUwzac24E/CTGf8iNvyHcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b4ed83576de2f47c4af5f61c1ae2a83556b6e7b5c6af17d9193e74a128f1f72","last_reissued_at":"2026-05-18T00:30:15.986571Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:15.986571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Aggregated Wasserstein Metric and State Registration for Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jia Li, Jianbo Ye, Yukun Chen","submitted_at":"2017-11-12T22:43:22Z","abstract_excerpt":"We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time position follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05792","kind":"arxiv","version":2},"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":"1711.05792","created_at":"2026-05-18T00:30:15.986713+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05792v2","created_at":"2026-05-18T00:30:15.986713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05792","created_at":"2026-05-18T00:30:15.986713+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNHNQNLW3YXU","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNHNQNLW3YXUPRFP","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNHNQNLW","created_at":"2026-05-18T12:31:08.081275+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/BNHNQNLW3YXUPRFPL5Q4DLRKQN","json":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN.json","graph_json":"https://pith.science/api/pith-number/BNHNQNLW3YXUPRFPL5Q4DLRKQN/graph.json","events_json":"https://pith.science/api/pith-number/BNHNQNLW3YXUPRFPL5Q4DLRKQN/events.json","paper":"https://pith.science/paper/BNHNQNLW"},"agent_actions":{"view_html":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN","download_json":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN.json","view_paper":"https://pith.science/paper/BNHNQNLW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05792&json=true","fetch_graph":"https://pith.science/api/pith-number/BNHNQNLW3YXUPRFPL5Q4DLRKQN/graph.json","fetch_events":"https://pith.science/api/pith-number/BNHNQNLW3YXUPRFPL5Q4DLRKQN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN/action/storage_attestation","attest_author":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN/action/author_attestation","sign_citation":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN/action/citation_signature","submit_replication":"https://pith.science/pith/BNHNQNLW3YXUPRFPL5Q4DLRKQN/action/replication_record"}},"created_at":"2026-05-18T00:30:15.986713+00:00","updated_at":"2026-05-18T00:30:15.986713+00:00"}