{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:E2MFZL6REOUFDZQGPUZTVU5ET7","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":"1f40f1d665f3839751a5a4d3514b9f2c06f3bc8f48cd1f86480813c694d5bd35","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2019-05-21T10:45:59Z","title_canon_sha256":"b06c15bda9978fd2861c7d0ff166dca9cbf44630a600953c19b7eff862df2205"},"schema_version":"1.0","source":{"id":"1905.08538","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.08538","created_at":"2026-05-17T23:45:42Z"},{"alias_kind":"arxiv_version","alias_value":"1905.08538v1","created_at":"2026-05-17T23:45:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.08538","created_at":"2026-05-17T23:45:42Z"},{"alias_kind":"pith_short_12","alias_value":"E2MFZL6REOUF","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"E2MFZL6REOUFDZQG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"E2MFZL6R","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:e127678deafed916aa3c3fc63568837cc537c5226a0140c89ad6aa95bee747e6","target":"graph","created_at":"2026-05-17T23:45:42Z","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":"High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initia","authors_text":"Raymond Chan, Tieyong Zeng, Xiaohao Cai, Xiaoyu Xie","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2019-05-21T10:45:59Z","title":"A Two-stage Classification Method for High-dimensional Data and Point Clouds"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.08538","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":"sha256:27081987a146593f623cf9a3000d22671e4fad8ae9ad9650d37aa907627577b1","target":"record","created_at":"2026-05-17T23:45:42Z","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":"1f40f1d665f3839751a5a4d3514b9f2c06f3bc8f48cd1f86480813c694d5bd35","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2019-05-21T10:45:59Z","title_canon_sha256":"b06c15bda9978fd2861c7d0ff166dca9cbf44630a600953c19b7eff862df2205"},"schema_version":"1.0","source":{"id":"1905.08538","kind":"arxiv","version":1}},"canonical_sha256":"26985cafd123a851e6067d333ad3a49fe9f4c5ce6db7b0ce70fa2d65a42fa73d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26985cafd123a851e6067d333ad3a49fe9f4c5ce6db7b0ce70fa2d65a42fa73d","first_computed_at":"2026-05-17T23:45:42.877935Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:42.877935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LWEn6yv69mCz5O7ZXUFg4PorCTuOX+Z/nzYyzMiWeP9zPC8cNCAvXiNqvu+FfVvFFIH/khNriTYRx34orHvLAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:42.878544Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.08538","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27081987a146593f623cf9a3000d22671e4fad8ae9ad9650d37aa907627577b1","sha256:e127678deafed916aa3c3fc63568837cc537c5226a0140c89ad6aa95bee747e6"],"state_sha256":"e9a7a191dcfb7579137c5affeb29da28c13e52eb1efe66dfb5caa4af599bafa0"}