{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:CA62HLUWI4ZLMIXS56LZTWOVND","short_pith_number":"pith:CA62HLUW","canonical_record":{"source":{"id":"1210.0699","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T08:40:46Z","cross_cats_sorted":[],"title_canon_sha256":"bc772ff7f80cdafe993658648c66594914735b2621c20c822207bb9e8c55af36","abstract_canon_sha256":"4d02b73e849f04411d6fbb96a249f516908900d81a2c2ab48613845e30fcd4ea"},"schema_version":"1.0"},"canonical_sha256":"103da3ae964732b622f2ef9799d9d568f52cd0ec25fc8f7191e10e41f2b72be5","source":{"kind":"arxiv","id":"1210.0699","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1210.0699","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"arxiv_version","alias_value":"1210.0699v1","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.0699","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"pith_short_12","alias_value":"CA62HLUWI4ZL","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CA62HLUWI4ZLMIXS","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CA62HLUW","created_at":"2026-05-18T12:27:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:CA62HLUWI4ZLMIXS56LZTWOVND","target":"record","payload":{"canonical_record":{"source":{"id":"1210.0699","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T08:40:46Z","cross_cats_sorted":[],"title_canon_sha256":"bc772ff7f80cdafe993658648c66594914735b2621c20c822207bb9e8c55af36","abstract_canon_sha256":"4d02b73e849f04411d6fbb96a249f516908900d81a2c2ab48613845e30fcd4ea"},"schema_version":"1.0"},"canonical_sha256":"103da3ae964732b622f2ef9799d9d568f52cd0ec25fc8f7191e10e41f2b72be5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:44:14.455355Z","signature_b64":"NiG6msCSS72X8hJaNE1UYb9va6R+r3ILnep3KqI1tPZ9AKKfiTNhAUjmRgYArIlxCdEyTCkjZkGvB0J+JbL+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"103da3ae964732b622f2ef9799d9d568f52cd0ec25fc8f7191e10e41f2b72be5","last_reissued_at":"2026-05-18T03:44:14.454876Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:44:14.454876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1210.0699","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":"2026-05-18T03:44:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sVdASPsmogzWplfFW3VGo7q1hi6UPOi/o1/d9j87XvMJ80Yb/W9yBrkAos8MHx25uvM9M/p7YrfKcNGU6p/fBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:47:15.622730Z"},"content_sha256":"c81c08f906aed968bbc0f7e101812fc401823ab949672264f9c30b8907b96291","schema_version":"1.0","event_id":"sha256:c81c08f906aed968bbc0f7e101812fc401823ab949672264f9c30b8907b96291"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:CA62HLUWI4ZLMIXS56LZTWOVND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ruiliang Zhang, Xavier Bresson","submitted_at":"2012-10-02T08:40:46Z","abstract_excerpt":"We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.0699","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":"2026-05-18T03:44:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JOOBvTRPACXrnhKCr0LI/DXHLPTVjzYCucWiVtrH+Y/pMQ5DIxOeqIP9ueuQcX/0Rhc6VDS6VEqm7QpBvP4ABA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:47:15.623388Z"},"content_sha256":"0d1467d2e837c07ffdd8f0cc4e225e52da51a31413d814d050cd10113dfc813c","schema_version":"1.0","event_id":"sha256:0d1467d2e837c07ffdd8f0cc4e225e52da51a31413d814d050cd10113dfc813c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CA62HLUWI4ZLMIXS56LZTWOVND/bundle.json","state_url":"https://pith.science/pith/CA62HLUWI4ZLMIXS56LZTWOVND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CA62HLUWI4ZLMIXS56LZTWOVND/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":"2026-05-30T15:47:15Z","links":{"resolver":"https://pith.science/pith/CA62HLUWI4ZLMIXS56LZTWOVND","bundle":"https://pith.science/pith/CA62HLUWI4ZLMIXS56LZTWOVND/bundle.json","state":"https://pith.science/pith/CA62HLUWI4ZLMIXS56LZTWOVND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CA62HLUWI4ZLMIXS56LZTWOVND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:CA62HLUWI4ZLMIXS56LZTWOVND","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":"4d02b73e849f04411d6fbb96a249f516908900d81a2c2ab48613845e30fcd4ea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T08:40:46Z","title_canon_sha256":"bc772ff7f80cdafe993658648c66594914735b2621c20c822207bb9e8c55af36"},"schema_version":"1.0","source":{"id":"1210.0699","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1210.0699","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"arxiv_version","alias_value":"1210.0699v1","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.0699","created_at":"2026-05-18T03:44:14Z"},{"alias_kind":"pith_short_12","alias_value":"CA62HLUWI4ZL","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CA62HLUWI4ZLMIXS","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CA62HLUW","created_at":"2026-05-18T12:27:01Z"}],"graph_snapshots":[{"event_id":"sha256:0d1467d2e837c07ffdd8f0cc4e225e52da51a31413d814d050cd10113dfc813c","target":"graph","created_at":"2026-05-18T03:44:14Z","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":"We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small.","authors_text":"Ruiliang Zhang, Xavier Bresson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T08:40:46Z","title":"TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.0699","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:c81c08f906aed968bbc0f7e101812fc401823ab949672264f9c30b8907b96291","target":"record","created_at":"2026-05-18T03:44:14Z","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":"4d02b73e849f04411d6fbb96a249f516908900d81a2c2ab48613845e30fcd4ea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T08:40:46Z","title_canon_sha256":"bc772ff7f80cdafe993658648c66594914735b2621c20c822207bb9e8c55af36"},"schema_version":"1.0","source":{"id":"1210.0699","kind":"arxiv","version":1}},"canonical_sha256":"103da3ae964732b622f2ef9799d9d568f52cd0ec25fc8f7191e10e41f2b72be5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"103da3ae964732b622f2ef9799d9d568f52cd0ec25fc8f7191e10e41f2b72be5","first_computed_at":"2026-05-18T03:44:14.454876Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:44:14.454876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NiG6msCSS72X8hJaNE1UYb9va6R+r3ILnep3KqI1tPZ9AKKfiTNhAUjmRgYArIlxCdEyTCkjZkGvB0J+JbL+Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:44:14.455355Z","signed_message":"canonical_sha256_bytes"},"source_id":"1210.0699","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c81c08f906aed968bbc0f7e101812fc401823ab949672264f9c30b8907b96291","sha256:0d1467d2e837c07ffdd8f0cc4e225e52da51a31413d814d050cd10113dfc813c"],"state_sha256":"fc3bfe9640c757513429487327580067cca167593562e77631e11f012745facc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"imnQ4eKRTNVr1fYKoAk5IheQkyERjDZLYCAUfPBwAWf2OPQ5bi3dmIU8oudmB0svj//WzxZqopaktvLcBmIeAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T15:47:15.626412Z","bundle_sha256":"14d6c92a06734c8ee61bc8c1cf8ba9102944d2795277fb805fdb057668e4b537"}}