{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:YUBUEGXSZEZUEODP7HNFT52VMX","short_pith_number":"pith:YUBUEGXS","canonical_record":{"source":{"id":"1709.01231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T04:10:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e73e020a2f83e52b60971061d85b9d94ea7547449b0a60f5161c4ad1136cb21d","abstract_canon_sha256":"f237921d867914ff863aabfbc01265728a689b8d1312e84ddbbf73508c432251"},"schema_version":"1.0"},"canonical_sha256":"c503421af2c93342386ff9da59f75565cb55e50169675e7b732ff91d0631fbf4","source":{"kind":"arxiv","id":"1709.01231","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.01231","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.01231v1","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01231","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"YUBUEGXSZEZU","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YUBUEGXSZEZUEODP","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YUBUEGXS","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:YUBUEGXSZEZUEODP7HNFT52VMX","target":"record","payload":{"canonical_record":{"source":{"id":"1709.01231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T04:10:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e73e020a2f83e52b60971061d85b9d94ea7547449b0a60f5161c4ad1136cb21d","abstract_canon_sha256":"f237921d867914ff863aabfbc01265728a689b8d1312e84ddbbf73508c432251"},"schema_version":"1.0"},"canonical_sha256":"c503421af2c93342386ff9da59f75565cb55e50169675e7b732ff91d0631fbf4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:00.611539Z","signature_b64":"p2ghbWdKsjxOGCsaqOC65uBNdqIECwfCetl5/9XzIV5wxIkMnRGyeUydcCvTeKwMCMmSaxjj9kj7sdX+ZJFyDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c503421af2c93342386ff9da59f75565cb55e50169675e7b732ff91d0631fbf4","last_reissued_at":"2026-05-18T00:36:00.611117Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:00.611117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.01231","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3rTqsFKu3CXyJUXNzLOlajbySihkm7RwGDxNJ6X3lPSsh5b/Z5oltM+efG0mvWgAG7UydbN9Rm3qvQzRbdgSBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:15:38.199500Z"},"content_sha256":"c9fa6f1747f42004c96f21a11c0205a6c617b3d07b47709f4c8bfbefd6ad8e57","schema_version":"1.0","event_id":"sha256:c9fa6f1747f42004c96f21a11c0205a6c617b3d07b47709f4c8bfbefd6ad8e57"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:YUBUEGXSZEZUEODP7HNFT52VMX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Discriminative Similarity for Clustering and Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Feng Liang, Jiashi Feng, Nebojsa Jojic, Shuicheng Yan, Thomas S. Huang, Yingzhen Yang","submitted_at":"2017-09-05T04:10:44Z","abstract_excerpt":"Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning. The proposed framework learns classifier from each hypothetical labeling, and searches for the optimal labeling by minimizing the generalization error of the learned classifiers associated with the hyp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01231","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0n6a8rtLI36m/VU0dIO31uHoppLwljjGzoAt+FZMY1C94m61dKhtxcbq5hHb8SEd5SZZerAfldUKuCFgUbfeDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:15:38.199845Z"},"content_sha256":"fb461f1f65bb380afd5e52a5293dc7e9d778916538b5321060596d168f1aa54b","schema_version":"1.0","event_id":"sha256:fb461f1f65bb380afd5e52a5293dc7e9d778916538b5321060596d168f1aa54b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YUBUEGXSZEZUEODP7HNFT52VMX/bundle.json","state_url":"https://pith.science/pith/YUBUEGXSZEZUEODP7HNFT52VMX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YUBUEGXSZEZUEODP7HNFT52VMX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T03:15:38Z","links":{"resolver":"https://pith.science/pith/YUBUEGXSZEZUEODP7HNFT52VMX","bundle":"https://pith.science/pith/YUBUEGXSZEZUEODP7HNFT52VMX/bundle.json","state":"https://pith.science/pith/YUBUEGXSZEZUEODP7HNFT52VMX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YUBUEGXSZEZUEODP7HNFT52VMX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:YUBUEGXSZEZUEODP7HNFT52VMX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f237921d867914ff863aabfbc01265728a689b8d1312e84ddbbf73508c432251","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T04:10:44Z","title_canon_sha256":"e73e020a2f83e52b60971061d85b9d94ea7547449b0a60f5161c4ad1136cb21d"},"schema_version":"1.0","source":{"id":"1709.01231","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.01231","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.01231v1","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01231","created_at":"2026-05-18T00:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"YUBUEGXSZEZU","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YUBUEGXSZEZUEODP","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YUBUEGXS","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:fb461f1f65bb380afd5e52a5293dc7e9d778916538b5321060596d168f1aa54b","target":"graph","created_at":"2026-05-18T00:36:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning. The proposed framework learns classifier from each hypothetical labeling, and searches for the optimal labeling by minimizing the generalization error of the learned classifiers associated with the hyp","authors_text":"Feng Liang, Jiashi Feng, Nebojsa Jojic, Shuicheng Yan, Thomas S. Huang, Yingzhen Yang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T04:10:44Z","title":"Discriminative Similarity for Clustering and Semi-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01231","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c9fa6f1747f42004c96f21a11c0205a6c617b3d07b47709f4c8bfbefd6ad8e57","target":"record","created_at":"2026-05-18T00:36:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f237921d867914ff863aabfbc01265728a689b8d1312e84ddbbf73508c432251","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T04:10:44Z","title_canon_sha256":"e73e020a2f83e52b60971061d85b9d94ea7547449b0a60f5161c4ad1136cb21d"},"schema_version":"1.0","source":{"id":"1709.01231","kind":"arxiv","version":1}},"canonical_sha256":"c503421af2c93342386ff9da59f75565cb55e50169675e7b732ff91d0631fbf4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c503421af2c93342386ff9da59f75565cb55e50169675e7b732ff91d0631fbf4","first_computed_at":"2026-05-18T00:36:00.611117Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:00.611117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p2ghbWdKsjxOGCsaqOC65uBNdqIECwfCetl5/9XzIV5wxIkMnRGyeUydcCvTeKwMCMmSaxjj9kj7sdX+ZJFyDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:00.611539Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.01231","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9fa6f1747f42004c96f21a11c0205a6c617b3d07b47709f4c8bfbefd6ad8e57","sha256:fb461f1f65bb380afd5e52a5293dc7e9d778916538b5321060596d168f1aa54b"],"state_sha256":"8aeb423a6f62b4684fc05fcf4a2d1bab5a756e497d3fc83f7ddfdb283818d96f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"As82BW/l5In9/IyGtGBGU8mDsYUxZWME8lah7w6rpN/VU8zG7j05bkPjXbdYMcaho4br87ce/vSH9BOHLw6cBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T03:15:38.201813Z","bundle_sha256":"a3d65f34df8c2cb03d66a1f1757cdf30a5d535d2a99797032458991e9ef85316"}}