{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:73FIPBYKEIOCMDQCIR225UGC53","short_pith_number":"pith:73FIPBYK","canonical_record":{"source":{"id":"1306.1326","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-06T07:42:33Z","cross_cats_sorted":[],"title_canon_sha256":"c2ea5fcd03e58afe16e6c897d6d75af9bc1f4f54a36724e9e28ba7e1ff730f0c","abstract_canon_sha256":"83db974dddaef09fae61dcd97691f4ce24d75875e689271845ff315ea5186921"},"schema_version":"1.0"},"canonical_sha256":"feca87870a221c260e024475aed0c2eee322a4241907e416c228776f679eb48a","source":{"kind":"arxiv","id":"1306.1326","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.1326","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"1306.1326v1","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.1326","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"73FIPBYKEIOC","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_16","alias_value":"73FIPBYKEIOCMDQC","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_8","alias_value":"73FIPBYK","created_at":"2026-05-18T12:27:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:73FIPBYKEIOCMDQCIR225UGC53","target":"record","payload":{"canonical_record":{"source":{"id":"1306.1326","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-06T07:42:33Z","cross_cats_sorted":[],"title_canon_sha256":"c2ea5fcd03e58afe16e6c897d6d75af9bc1f4f54a36724e9e28ba7e1ff730f0c","abstract_canon_sha256":"83db974dddaef09fae61dcd97691f4ce24d75875e689271845ff315ea5186921"},"schema_version":"1.0"},"canonical_sha256":"feca87870a221c260e024475aed0c2eee322a4241907e416c228776f679eb48a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:21:34.603931Z","signature_b64":"CMu/WIX8ONkWnS3kyHQy7sOaGnx9TQWLnofYijaWIP7TA0nXwvsl13Ypmnrq1KWu+4lrpgINojCBzEbO9UgzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"feca87870a221c260e024475aed0c2eee322a4241907e416c228776f679eb48a","last_reissued_at":"2026-05-18T03:21:34.603425Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:21:34.603425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1306.1326","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":"2026-05-18T03:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2BERR8szXDvyBBhFvwQ+tBcw8J/Q9NA4Hgkrpw2a7dcVsmMYIND9vUuw6/v50+YkjnWl4roJGFeXOVz0klRvAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T04:47:34.798327Z"},"content_sha256":"51241ca03472f2bd3ee77fb94387ad284716ff4822087d55adfd812007d36ddf","schema_version":"1.0","event_id":"sha256:51241ca03472f2bd3ee77fb94387ad284716ff4822087d55adfd812007d36ddf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:73FIPBYKEIOCMDQCIR225UGC53","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Performance analysis of unsupervised feature selection methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"A. Nisthana Parveen, E.N. Sathishkumar, H. Hannah Inbarani","submitted_at":"2013-06-06T07:42:33Z","abstract_excerpt":"Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification. Feature selection can remedy this problem and therefore improve the prediction accuracy and reduce the computational overhead of classification algorithms. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.1326","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":"2026-05-18T03:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aAz+JvqwZ8os2Q1PoP1huqduCrHNYcOqRJcU+jJM3wWY7tWfjyEZKUIapK9Bn7YLPFWs+6WqH6LLC1OzkewSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T04:47:34.798677Z"},"content_sha256":"f8ad7b62b8bd7124fe84a53f3b7c4aac7af6ddf9f621a6424e313218a5800d5f","schema_version":"1.0","event_id":"sha256:f8ad7b62b8bd7124fe84a53f3b7c4aac7af6ddf9f621a6424e313218a5800d5f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/73FIPBYKEIOCMDQCIR225UGC53/bundle.json","state_url":"https://pith.science/pith/73FIPBYKEIOCMDQCIR225UGC53/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/73FIPBYKEIOCMDQCIR225UGC53/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":"2026-07-03T04:47:34Z","links":{"resolver":"https://pith.science/pith/73FIPBYKEIOCMDQCIR225UGC53","bundle":"https://pith.science/pith/73FIPBYKEIOCMDQCIR225UGC53/bundle.json","state":"https://pith.science/pith/73FIPBYKEIOCMDQCIR225UGC53/state.json","well_known_bundle":"https://pith.science/.well-known/pith/73FIPBYKEIOCMDQCIR225UGC53/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:73FIPBYKEIOCMDQCIR225UGC53","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":"83db974dddaef09fae61dcd97691f4ce24d75875e689271845ff315ea5186921","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-06T07:42:33Z","title_canon_sha256":"c2ea5fcd03e58afe16e6c897d6d75af9bc1f4f54a36724e9e28ba7e1ff730f0c"},"schema_version":"1.0","source":{"id":"1306.1326","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.1326","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"1306.1326v1","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.1326","created_at":"2026-05-18T03:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"73FIPBYKEIOC","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_16","alias_value":"73FIPBYKEIOCMDQC","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_8","alias_value":"73FIPBYK","created_at":"2026-05-18T12:27:36Z"}],"graph_snapshots":[{"event_id":"sha256:f8ad7b62b8bd7124fe84a53f3b7c4aac7af6ddf9f621a6424e313218a5800d5f","target":"graph","created_at":"2026-05-18T03:21:34Z","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":"Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification. Feature selection can remedy this problem and therefore improve the prediction accuracy and reduce the computational overhead of classification algorithms. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised","authors_text":"A. Nisthana Parveen, E.N. Sathishkumar, H. Hannah Inbarani","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-06T07:42:33Z","title":"Performance analysis of unsupervised feature selection methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.1326","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":"sha256:51241ca03472f2bd3ee77fb94387ad284716ff4822087d55adfd812007d36ddf","target":"record","created_at":"2026-05-18T03:21:34Z","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":"83db974dddaef09fae61dcd97691f4ce24d75875e689271845ff315ea5186921","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-06T07:42:33Z","title_canon_sha256":"c2ea5fcd03e58afe16e6c897d6d75af9bc1f4f54a36724e9e28ba7e1ff730f0c"},"schema_version":"1.0","source":{"id":"1306.1326","kind":"arxiv","version":1}},"canonical_sha256":"feca87870a221c260e024475aed0c2eee322a4241907e416c228776f679eb48a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"feca87870a221c260e024475aed0c2eee322a4241907e416c228776f679eb48a","first_computed_at":"2026-05-18T03:21:34.603425Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:21:34.603425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CMu/WIX8ONkWnS3kyHQy7sOaGnx9TQWLnofYijaWIP7TA0nXwvsl13Ypmnrq1KWu+4lrpgINojCBzEbO9UgzDA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:21:34.603931Z","signed_message":"canonical_sha256_bytes"},"source_id":"1306.1326","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51241ca03472f2bd3ee77fb94387ad284716ff4822087d55adfd812007d36ddf","sha256:f8ad7b62b8bd7124fe84a53f3b7c4aac7af6ddf9f621a6424e313218a5800d5f"],"state_sha256":"481b4f43791d1b5822f6ffd266215d8f93064870a10f4c3bc78bb5f0a0058e7c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5mebHRyr5VVOl4x6h/ITWiojGD/oxYBdVZIA6vtc6h/tqIP1wTHmFEOI9CZuaKNjCp87ZKygy10YBofKbl65Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T04:47:34.800531Z","bundle_sha256":"7f19b89b8d9a7c8a5065e0c921bd9a984b9c9281b5582ad35f4ff394bed4d09b"}}