{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:B3PHWPG5NPDXCIMKDSV2HVBOUE","short_pith_number":"pith:B3PHWPG5","canonical_record":{"source":{"id":"1405.6444","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-05-26T01:15:44Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"776c87dba717e866ddbfb849de5ac1c16d54adda91c5d0e30830be4325fff521","abstract_canon_sha256":"68d1ad238f540b555d8d66d4fd7adcf1898b88ca094fd6fde9366be9287133f4"},"schema_version":"1.0"},"canonical_sha256":"0ede7b3cdd6bc771218a1caba3d42ea10f91df5a728dc21951c84250f6c29909","source":{"kind":"arxiv","id":"1405.6444","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.6444","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"arxiv_version","alias_value":"1405.6444v1","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.6444","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"pith_short_12","alias_value":"B3PHWPG5NPDX","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"B3PHWPG5NPDXCIMK","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"B3PHWPG5","created_at":"2026-05-18T12:28:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:B3PHWPG5NPDXCIMKDSV2HVBOUE","target":"record","payload":{"canonical_record":{"source":{"id":"1405.6444","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-05-26T01:15:44Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"776c87dba717e866ddbfb849de5ac1c16d54adda91c5d0e30830be4325fff521","abstract_canon_sha256":"68d1ad238f540b555d8d66d4fd7adcf1898b88ca094fd6fde9366be9287133f4"},"schema_version":"1.0"},"canonical_sha256":"0ede7b3cdd6bc771218a1caba3d42ea10f91df5a728dc21951c84250f6c29909","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:07.297972Z","signature_b64":"BN8Nrq7GN5nO74Ci0RPoWmdx2ctPmuI5wOKyXnJXkH+cOuGBww6eXMfH3Qkwz/05S8oYYezkWGUP4DjJ51uTCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ede7b3cdd6bc771218a1caba3d42ea10f91df5a728dc21951c84250f6c29909","last_reissued_at":"2026-05-18T02:51:07.297489Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:07.297489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.6444","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":"2026-05-18T02:51:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EqqQ06qm6CHCMGLIm70KjAb7+FwsBbhcA5//JrIt5fCWpZQEVdaL4vzQcG8yU+ZxkxzcSRtu042LKETEHNABBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:30:18.119850Z"},"content_sha256":"5ad978bb0a2a129b2e25c430b70f2c66389556627c4ba19499d1f6e57997c3e2","schema_version":"1.0","event_id":"sha256:5ad978bb0a2a129b2e25c430b70f2c66389556627c4ba19499d1f6e57997c3e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:B3PHWPG5NPDXCIMKDSV2HVBOUE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The role of dimensionality reduction in linear classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Miguel \\'A. Carreira-Perpi\\~n\\'an, Weiran Wang","submitted_at":"2014-05-26T01:15:44Z","abstract_excerpt":"Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns a classifier (a filter approach). Best performance would be obtained by optimizing the classification error jointly over DR mapping and classifier (a wrapper approach), but this is a difficult nonconvex problem, particularly with nonlinear DR. Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.6444","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":"2026-05-18T02:51:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W17u9uDR+n/57oHUZ7V0EXgJDAegAovV7PAHjugb4r13DFzDHoQt+TEEyBlv2ssbYI4y3bWhgEha1fucbfaLAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:30:18.120488Z"},"content_sha256":"da8562bd3e1ab8b4db399336e33d762cd8cb536671f0d2331d4e4622b16d00a4","schema_version":"1.0","event_id":"sha256:da8562bd3e1ab8b4db399336e33d762cd8cb536671f0d2331d4e4622b16d00a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/bundle.json","state_url":"https://pith.science/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/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":"2026-06-07T17:30:18Z","links":{"resolver":"https://pith.science/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE","bundle":"https://pith.science/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/bundle.json","state":"https://pith.science/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B3PHWPG5NPDXCIMKDSV2HVBOUE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:B3PHWPG5NPDXCIMKDSV2HVBOUE","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":"68d1ad238f540b555d8d66d4fd7adcf1898b88ca094fd6fde9366be9287133f4","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-05-26T01:15:44Z","title_canon_sha256":"776c87dba717e866ddbfb849de5ac1c16d54adda91c5d0e30830be4325fff521"},"schema_version":"1.0","source":{"id":"1405.6444","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.6444","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"arxiv_version","alias_value":"1405.6444v1","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.6444","created_at":"2026-05-18T02:51:07Z"},{"alias_kind":"pith_short_12","alias_value":"B3PHWPG5NPDX","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"B3PHWPG5NPDXCIMK","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"B3PHWPG5","created_at":"2026-05-18T12:28:19Z"}],"graph_snapshots":[{"event_id":"sha256:da8562bd3e1ab8b4db399336e33d762cd8cb536671f0d2331d4e4622b16d00a4","target":"graph","created_at":"2026-05-18T02:51:07Z","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":"Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns a classifier (a filter approach). Best performance would be obtained by optimizing the classification error jointly over DR mapping and classifier (a wrapper approach), but this is a difficult nonconvex problem, particularly with nonlinear DR. Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a ","authors_text":"Miguel \\'A. Carreira-Perpi\\~n\\'an, Weiran Wang","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-05-26T01:15:44Z","title":"The role of dimensionality reduction in linear classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.6444","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":"sha256:5ad978bb0a2a129b2e25c430b70f2c66389556627c4ba19499d1f6e57997c3e2","target":"record","created_at":"2026-05-18T02:51:07Z","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":"68d1ad238f540b555d8d66d4fd7adcf1898b88ca094fd6fde9366be9287133f4","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-05-26T01:15:44Z","title_canon_sha256":"776c87dba717e866ddbfb849de5ac1c16d54adda91c5d0e30830be4325fff521"},"schema_version":"1.0","source":{"id":"1405.6444","kind":"arxiv","version":1}},"canonical_sha256":"0ede7b3cdd6bc771218a1caba3d42ea10f91df5a728dc21951c84250f6c29909","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ede7b3cdd6bc771218a1caba3d42ea10f91df5a728dc21951c84250f6c29909","first_computed_at":"2026-05-18T02:51:07.297489Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:51:07.297489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BN8Nrq7GN5nO74Ci0RPoWmdx2ctPmuI5wOKyXnJXkH+cOuGBww6eXMfH3Qkwz/05S8oYYezkWGUP4DjJ51uTCg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:51:07.297972Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.6444","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ad978bb0a2a129b2e25c430b70f2c66389556627c4ba19499d1f6e57997c3e2","sha256:da8562bd3e1ab8b4db399336e33d762cd8cb536671f0d2331d4e4622b16d00a4"],"state_sha256":"b5e91aa6661287c8ca4ffa5200f84b3c8cc7310619b84e6041d4ef581a9d4755"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m962maANeMJ91o3HUdIZXHPvIFqWIKRpmlRq7yGJHhlkSydf5dK8DS9t+VDIv3EIxVAzqy4b5LB2TwSZScXvBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:30:18.123902Z","bundle_sha256":"0f1d274f2005b20172d16803b134c890739b9cfb398a71b17cc14fee6fde8b9c"}}