{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:F6OMMPSAX2MLYYTGLKFJSTF7M6","short_pith_number":"pith:F6OMMPSA","canonical_record":{"source":{"id":"1412.6506","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-12-19T20:06:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2b52cd90d7d96afde46de7c8020288c125834929bc6255d9e6d74d82e94cf751","abstract_canon_sha256":"615b7821a58148b916018175b86ceee8c6e806c054d6571e36df7715f4ea0075"},"schema_version":"1.0"},"canonical_sha256":"2f9cc63e40be98bc62665a8a994cbf678f45e18f612de74862badf3be7eac44a","source":{"kind":"arxiv","id":"1412.6506","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.6506","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"arxiv_version","alias_value":"1412.6506v1","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.6506","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"pith_short_12","alias_value":"F6OMMPSAX2ML","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F6OMMPSAX2MLYYTG","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F6OMMPSA","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:F6OMMPSAX2MLYYTGLKFJSTF7M6","target":"record","payload":{"canonical_record":{"source":{"id":"1412.6506","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-12-19T20:06:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2b52cd90d7d96afde46de7c8020288c125834929bc6255d9e6d74d82e94cf751","abstract_canon_sha256":"615b7821a58148b916018175b86ceee8c6e806c054d6571e36df7715f4ea0075"},"schema_version":"1.0"},"canonical_sha256":"2f9cc63e40be98bc62665a8a994cbf678f45e18f612de74862badf3be7eac44a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:30:48.228729Z","signature_b64":"uKAkpZd8qG4aTBY1KRtYiROoDN7BMA3iN470SR4/gjL3LmMviRD1nlW8RO0XTh3LBLaRpo5WqYyVdvrACCV2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f9cc63e40be98bc62665a8a994cbf678f45e18f612de74862badf3be7eac44a","last_reissued_at":"2026-05-18T02:30:48.228299Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:30:48.228299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.6506","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":"2026-05-18T02:30:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xRlOTl4h8v0cyQMgCA06gkqN7JMIMSkVVhf5FFZqFllVgPlRghvIzvGK/FUxqrEjcNiFyed7+ayApLClLBeSBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T07:11:45.001094Z"},"content_sha256":"509e3e497e4124b4d06aa59f4b7538ec3cca075ce02160c35ae8c78f926bbc40","schema_version":"1.0","event_id":"sha256:509e3e497e4124b4d06aa59f4b7538ec3cca075ce02160c35ae8c78f926bbc40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:F6OMMPSAX2MLYYTGLKFJSTF7M6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cauchy Principal Component Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric Xing, Pengtao Xie","submitted_at":"2014-12-19T20:06:02Z","abstract_excerpt":"Principal Component Analysis (PCA) has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively. In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise. We utilize Cauchy distribution to model noise and derive Cauchy PCA under the maximum likelihood estimation (MLE) framework with low rank constraint. Our method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.6506","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":"2026-05-18T02:30:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7QJEwpqqL809eiy3B3Si5RbZ8qZAS95EdChP2lPWQRtZe2WamMLEV6rWI5SAvFHZOEIBSuyDgpvXyAagSpT9DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T07:11:45.001889Z"},"content_sha256":"9ce104b680036b43e09fcd56d49146001ee990b7075911b2dc0208dfcf4ce1dc","schema_version":"1.0","event_id":"sha256:9ce104b680036b43e09fcd56d49146001ee990b7075911b2dc0208dfcf4ce1dc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/bundle.json","state_url":"https://pith.science/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/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":"2026-06-10T07:11:45Z","links":{"resolver":"https://pith.science/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6","bundle":"https://pith.science/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/bundle.json","state":"https://pith.science/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F6OMMPSAX2MLYYTGLKFJSTF7M6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:F6OMMPSAX2MLYYTGLKFJSTF7M6","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":"615b7821a58148b916018175b86ceee8c6e806c054d6571e36df7715f4ea0075","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-12-19T20:06:02Z","title_canon_sha256":"2b52cd90d7d96afde46de7c8020288c125834929bc6255d9e6d74d82e94cf751"},"schema_version":"1.0","source":{"id":"1412.6506","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.6506","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"arxiv_version","alias_value":"1412.6506v1","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.6506","created_at":"2026-05-18T02:30:48Z"},{"alias_kind":"pith_short_12","alias_value":"F6OMMPSAX2ML","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"F6OMMPSAX2MLYYTG","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"F6OMMPSA","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:9ce104b680036b43e09fcd56d49146001ee990b7075911b2dc0208dfcf4ce1dc","target":"graph","created_at":"2026-05-18T02:30:48Z","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":"Principal Component Analysis (PCA) has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively. In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise. We utilize Cauchy distribution to model noise and derive Cauchy PCA under the maximum likelihood estimation (MLE) framework with low rank constraint. Our method","authors_text":"Eric Xing, Pengtao Xie","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-12-19T20:06:02Z","title":"Cauchy Principal Component Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.6506","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":"sha256:509e3e497e4124b4d06aa59f4b7538ec3cca075ce02160c35ae8c78f926bbc40","target":"record","created_at":"2026-05-18T02:30:48Z","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":"615b7821a58148b916018175b86ceee8c6e806c054d6571e36df7715f4ea0075","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-12-19T20:06:02Z","title_canon_sha256":"2b52cd90d7d96afde46de7c8020288c125834929bc6255d9e6d74d82e94cf751"},"schema_version":"1.0","source":{"id":"1412.6506","kind":"arxiv","version":1}},"canonical_sha256":"2f9cc63e40be98bc62665a8a994cbf678f45e18f612de74862badf3be7eac44a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f9cc63e40be98bc62665a8a994cbf678f45e18f612de74862badf3be7eac44a","first_computed_at":"2026-05-18T02:30:48.228299Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:30:48.228299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uKAkpZd8qG4aTBY1KRtYiROoDN7BMA3iN470SR4/gjL3LmMviRD1nlW8RO0XTh3LBLaRpo5WqYyVdvrACCV2AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:30:48.228729Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.6506","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:509e3e497e4124b4d06aa59f4b7538ec3cca075ce02160c35ae8c78f926bbc40","sha256:9ce104b680036b43e09fcd56d49146001ee990b7075911b2dc0208dfcf4ce1dc"],"state_sha256":"b47c508d24537ca67417e57c792f7e7a929d625c31cac9a5c7e99ed15e3c2021"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wkvtnl/vt6HxEwb5PfEd2lYF/KfP20hksAY7Ri9Cps5AP0Jh09XaSU4kf00Bj36aYZ+bk24wGyM1Xl010NyaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T07:11:45.005942Z","bundle_sha256":"c5c0ec16a80c0d6b29a754273fc09e6eaa5f5e9122938f82246a2c29a00b3193"}}