{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JKQNC7H5TL74F3K644ZODJJTKF","short_pith_number":"pith:JKQNC7H5","schema_version":"1.0","canonical_sha256":"4aa0d17cfd9affc2ed5ee732e1a533517322f02387ce58e241d5d3fa3fdff9d5","source":{"kind":"arxiv","id":"2201.09267","version":1},"attestation_state":"computed","paper":{"title":"Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Ghodsi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley","submitted_at":"2022-01-23T13:53:23Z","abstract_excerpt":"This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and generalized Mahalanobis distance. In spectral methods, we start with methods using scatters of data, including the first spectral metric learning, relevant methods to Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric learning, imbalanced metric learning, locally linear metric"},"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":"2201.09267","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-01-23T13:53:23Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1cce8a2541dfbed4808a5a69793c0bcce80b55617a60c405d723328f02ff06cf","abstract_canon_sha256":"041713312bbd31d6de007665bf49cc977d0e409d8d0f9fc5a1edc7503e9aef8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:50:55.815423Z","signature_b64":"ZcJzpplwqDR+QqCzDWrMggXI/ZWoyWPpDzS5yMKatdf24xO262dAhN5Q/UX8TIufTS5SDdRNEB+HWYFp4xrjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4aa0d17cfd9affc2ed5ee732e1a533517322f02387ce58e241d5d3fa3fdff9d5","last_reissued_at":"2026-07-05T03:50:55.814941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:50:55.814941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Ghodsi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley","submitted_at":"2022-01-23T13:53:23Z","abstract_excerpt":"This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and generalized Mahalanobis distance. In spectral methods, we start with methods using scatters of data, including the first spectral metric learning, relevant methods to Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric learning, imbalanced metric learning, locally linear metric"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.09267","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/2201.09267/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":"2201.09267","created_at":"2026-07-05T03:50:55.814998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.09267v1","created_at":"2026-07-05T03:50:55.814998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.09267","created_at":"2026-07-05T03:50:55.814998+00:00"},{"alias_kind":"pith_short_12","alias_value":"JKQNC7H5TL74","created_at":"2026-07-05T03:50:55.814998+00:00"},{"alias_kind":"pith_short_16","alias_value":"JKQNC7H5TL74F3K6","created_at":"2026-07-05T03:50:55.814998+00:00"},{"alias_kind":"pith_short_8","alias_value":"JKQNC7H5","created_at":"2026-07-05T03:50:55.814998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24985","citing_title":"Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection","ref_index":157,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02279","citing_title":"Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF","json":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF.json","graph_json":"https://pith.science/api/pith-number/JKQNC7H5TL74F3K644ZODJJTKF/graph.json","events_json":"https://pith.science/api/pith-number/JKQNC7H5TL74F3K644ZODJJTKF/events.json","paper":"https://pith.science/paper/JKQNC7H5"},"agent_actions":{"view_html":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF","download_json":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF.json","view_paper":"https://pith.science/paper/JKQNC7H5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.09267&json=true","fetch_graph":"https://pith.science/api/pith-number/JKQNC7H5TL74F3K644ZODJJTKF/graph.json","fetch_events":"https://pith.science/api/pith-number/JKQNC7H5TL74F3K644ZODJJTKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF/action/storage_attestation","attest_author":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF/action/author_attestation","sign_citation":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF/action/citation_signature","submit_replication":"https://pith.science/pith/JKQNC7H5TL74F3K644ZODJJTKF/action/replication_record"}},"created_at":"2026-07-05T03:50:55.814998+00:00","updated_at":"2026-07-05T03:50:55.814998+00:00"}