{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:3SKJRZHSW3NYDYHZVH6V4OAW2Q","short_pith_number":"pith:3SKJRZHS","canonical_record":{"source":{"id":"1501.00857","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T13:37:37Z","cross_cats_sorted":[],"title_canon_sha256":"74e96ef2d7ffc2c5acc6496d29114186bed5c563ad16438b1ea4d436ecac29e3","abstract_canon_sha256":"679dbf9ed52d7c66e2af58954f9eb1989b06e2c63dccbebb9137f799949fb596"},"schema_version":"1.0"},"canonical_sha256":"dc9498e4f2b6db81e0f9a9fd5e3816d404c835efc93f4f4bc1058bdab45c3503","source":{"kind":"arxiv","id":"1501.00857","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.00857","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"arxiv_version","alias_value":"1501.00857v1","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.00857","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"pith_short_12","alias_value":"3SKJRZHSW3NY","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3SKJRZHSW3NYDYHZ","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3SKJRZHS","created_at":"2026-05-18T12:29:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:3SKJRZHSW3NYDYHZVH6V4OAW2Q","target":"record","payload":{"canonical_record":{"source":{"id":"1501.00857","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T13:37:37Z","cross_cats_sorted":[],"title_canon_sha256":"74e96ef2d7ffc2c5acc6496d29114186bed5c563ad16438b1ea4d436ecac29e3","abstract_canon_sha256":"679dbf9ed52d7c66e2af58954f9eb1989b06e2c63dccbebb9137f799949fb596"},"schema_version":"1.0"},"canonical_sha256":"dc9498e4f2b6db81e0f9a9fd5e3816d404c835efc93f4f4bc1058bdab45c3503","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:30:01.695761Z","signature_b64":"FPmt/w7+UVoWR6LTCsHszxQ53+4XQdZBwHQqoJnn6RpoNkddAZzO1vX4rL/ujXxPKSnyO6MkeKSHMaePqbaJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc9498e4f2b6db81e0f9a9fd5e3816d404c835efc93f4f4bc1058bdab45c3503","last_reissued_at":"2026-05-18T02:30:01.695338Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:30:01.695338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1501.00857","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":"2026-05-18T02:30:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aIvKHzBiDp74/BqFGyetIQvk94s2lugefg1zOWORgP/edHEBajbgkBXtHFGc8k2s9F6M2uzEYSK3Pqz9Aq+sAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T03:41:20.016709Z"},"content_sha256":"aaa088ec3ccc0d5d4c3f627c0df8326e96eeef68a5813995fa16a0a9c63fc4d2","schema_version":"1.0","event_id":"sha256:aaa088ec3ccc0d5d4c3f627c0df8326e96eeef68a5813995fa16a0a9c63fc4d2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:3SKJRZHSW3NYDYHZVH6V4OAW2Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast forward feature selection for the nonlinear classification of hyperspectral images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anthony Zullo, Clement Dechesne, Fr\\'ed\\'eric Ferraty, Mathieu Fauvel","submitted_at":"2015-01-05T13:37:37Z","abstract_excerpt":"A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.00857","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":"2026-05-18T02:30:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qMpZiIKwOAi7hM1s9O6pfbaJL711MF2P6znll086GK8DueflQldKY/5rHBob/4LAyfvQgIC69/KAJY3KSLJ9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T03:41:20.017055Z"},"content_sha256":"71a783cd8cfac704d54cabe86449a3e9d077f2c8afc48141fd3ca40a9f74d19b","schema_version":"1.0","event_id":"sha256:71a783cd8cfac704d54cabe86449a3e9d077f2c8afc48141fd3ca40a9f74d19b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/bundle.json","state_url":"https://pith.science/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/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":"2026-06-23T03:41:20Z","links":{"resolver":"https://pith.science/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q","bundle":"https://pith.science/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/bundle.json","state":"https://pith.science/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3SKJRZHSW3NYDYHZVH6V4OAW2Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:3SKJRZHSW3NYDYHZVH6V4OAW2Q","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":"679dbf9ed52d7c66e2af58954f9eb1989b06e2c63dccbebb9137f799949fb596","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T13:37:37Z","title_canon_sha256":"74e96ef2d7ffc2c5acc6496d29114186bed5c563ad16438b1ea4d436ecac29e3"},"schema_version":"1.0","source":{"id":"1501.00857","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.00857","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"arxiv_version","alias_value":"1501.00857v1","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.00857","created_at":"2026-05-18T02:30:01Z"},{"alias_kind":"pith_short_12","alias_value":"3SKJRZHSW3NY","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3SKJRZHSW3NYDYHZ","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3SKJRZHS","created_at":"2026-05-18T12:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:71a783cd8cfac704d54cabe86449a3e9d077f2c8afc48141fd3ca40a9f74d19b","target":"graph","created_at":"2026-05-18T02:30:01Z","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":"A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalizati","authors_text":"Anthony Zullo, Clement Dechesne, Fr\\'ed\\'eric Ferraty, Mathieu Fauvel","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T13:37:37Z","title":"Fast forward feature selection for the nonlinear classification of hyperspectral images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.00857","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:aaa088ec3ccc0d5d4c3f627c0df8326e96eeef68a5813995fa16a0a9c63fc4d2","target":"record","created_at":"2026-05-18T02:30:01Z","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":"679dbf9ed52d7c66e2af58954f9eb1989b06e2c63dccbebb9137f799949fb596","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T13:37:37Z","title_canon_sha256":"74e96ef2d7ffc2c5acc6496d29114186bed5c563ad16438b1ea4d436ecac29e3"},"schema_version":"1.0","source":{"id":"1501.00857","kind":"arxiv","version":1}},"canonical_sha256":"dc9498e4f2b6db81e0f9a9fd5e3816d404c835efc93f4f4bc1058bdab45c3503","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc9498e4f2b6db81e0f9a9fd5e3816d404c835efc93f4f4bc1058bdab45c3503","first_computed_at":"2026-05-18T02:30:01.695338Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:30:01.695338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FPmt/w7+UVoWR6LTCsHszxQ53+4XQdZBwHQqoJnn6RpoNkddAZzO1vX4rL/ujXxPKSnyO6MkeKSHMaePqbaJCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:30:01.695761Z","signed_message":"canonical_sha256_bytes"},"source_id":"1501.00857","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aaa088ec3ccc0d5d4c3f627c0df8326e96eeef68a5813995fa16a0a9c63fc4d2","sha256:71a783cd8cfac704d54cabe86449a3e9d077f2c8afc48141fd3ca40a9f74d19b"],"state_sha256":"1a91825f87b4037f72ebeb0d4078505de216fc61e99d3ed66d2ad8699d67f495"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"omnFL54EGiIReu0ceXkuzqjy46O3XMoCK8ftP6gI5ojwrRJFfyr0TpQkTTglTLNoVyRHW0E/dbe2i7pJUhAZAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T03:41:20.018998Z","bundle_sha256":"93fb2b4034ed7a73c8bcdaee986659a703ce93c4edf09e184a606027a6ae66b9"}}