{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FFGU3RB4PUUGZR6GK6BK5JVZ4E","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":"32c060489e8c12a50ad0e516f2c5685a47ad76aca6989823e91d8509a97a0630","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-26T14:02:21Z","title_canon_sha256":"092d60393f2a98e0a9c17c3d7b359991d9173760c3ff265f16d20136daee51d6"},"schema_version":"1.0","source":{"id":"1902.09938","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09938","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09938v1","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09938","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"pith_short_12","alias_value":"FFGU3RB4PUUG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FFGU3RB4PUUGZR6G","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FFGU3RB4","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:01e51c798c5b819c1b90b8863a09738be95ac4e81a7825d0414d222bf1d8bacb","target":"graph","created_at":"2026-05-17T23:52:35Z","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":"Consider a supervised dataset $D=[A\\mid \\textbf{b}]$, where $\\textbf{b}$ is the outcome column, rows of $D$ correspond to observations, and columns of $A$ are the features of the dataset. A central problem in machine learning and pattern recognition is to select the most important features from $D$ to be able to predict the outcome. In this paper, we provide a new feature selection method where we use perturbation theory to detect correlations between features. We solve $AX=\\textbf{b}$ using the method of least squares and singular value decomposition of $A$. In practical applications, such as","authors_text":"Hamid Usefi, Javad Rahimipour Anaraki","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-26T14:02:21Z","title":"A Feature Selection Based on Perturbation Theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09938","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":"sha256:722308850d5560e6a9c0665aea0eb3c54fc02ce3c47cb9a935b49e342b1fc39e","target":"record","created_at":"2026-05-17T23:52:35Z","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":"32c060489e8c12a50ad0e516f2c5685a47ad76aca6989823e91d8509a97a0630","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-26T14:02:21Z","title_canon_sha256":"092d60393f2a98e0a9c17c3d7b359991d9173760c3ff265f16d20136daee51d6"},"schema_version":"1.0","source":{"id":"1902.09938","kind":"arxiv","version":1}},"canonical_sha256":"294d4dc43c7d286cc7c65782aea6b9e10827f5c7bdb36fccc867bf327c9cee57","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"294d4dc43c7d286cc7c65782aea6b9e10827f5c7bdb36fccc867bf327c9cee57","first_computed_at":"2026-05-17T23:52:35.015501Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:35.015501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IxcKFcYQ4qUcnar4Eijhb3/kvUEw0y6/9U8D0JFxiFNDX2QFUT+siKsvfLp9qnEk7yDHBXDbh+JV7jNETnQIAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:35.015853Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.09938","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:722308850d5560e6a9c0665aea0eb3c54fc02ce3c47cb9a935b49e342b1fc39e","sha256:01e51c798c5b819c1b90b8863a09738be95ac4e81a7825d0414d222bf1d8bacb"],"state_sha256":"c61253d9c6fd133628334644a98de9f28219bf870cc15c7964960f3e6c2cfb44"}