{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GV5GTLRN2UM72MPV2DG7AZ6BRB","short_pith_number":"pith:GV5GTLRN","schema_version":"1.0","canonical_sha256":"357a69ae2dd519fd31f5d0cdf067c188712ac1180c764a6075f5a3e7442c9a96","source":{"kind":"arxiv","id":"1904.06491","version":1},"attestation_state":"computed","paper":{"title":"Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aruna Tiwari, Chandan Gautam, M. Tanveer","submitted_at":"2019-04-13T06:37:34Z","abstract_excerpt":"A brain can detect outlier just by using only normal samples. Similarly, one-class classification (OCC) also uses only normal samples to train the model and trained model can be used for outlier detection. In this paper, a multi-layer architecture for OCC is proposed by stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion. These Auto-Encoders are formulated under two types of Graph-Embedding, namely, local and global variance-based embedding. This Graph-Embedding explores the relationship between samples and multi-layers of Auto-Encoder pr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.06491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-13T06:37:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"078b5324ed8f5c0140ae64d248ef6f73610b9eca610ddcafbb251a9494991b4c","abstract_canon_sha256":"9e36ea773925324429df20b02e19784623c4e2144da1dea6316311aa5deea9d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:39.039794Z","signature_b64":"Aup03OXSvvQaHzF6IuhN8KUsbinm3v+zUQ3HC2RiqxrjLlsWpJHKhs96gZfwZ9DzzkJ/oP7+EAT973qNO5FZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"357a69ae2dd519fd31f5d0cdf067c188712ac1180c764a6075f5a3e7442c9a96","last_reissued_at":"2026-05-17T23:48:39.039114Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:39.039114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aruna Tiwari, Chandan Gautam, M. Tanveer","submitted_at":"2019-04-13T06:37:34Z","abstract_excerpt":"A brain can detect outlier just by using only normal samples. Similarly, one-class classification (OCC) also uses only normal samples to train the model and trained model can be used for outlier detection. In this paper, a multi-layer architecture for OCC is proposed by stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion. These Auto-Encoders are formulated under two types of Graph-Embedding, namely, local and global variance-based embedding. This Graph-Embedding explores the relationship between samples and multi-layers of Auto-Encoder pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06491","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.06491","created_at":"2026-05-17T23:48:39.039214+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.06491v1","created_at":"2026-05-17T23:48:39.039214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06491","created_at":"2026-05-17T23:48:39.039214+00:00"},{"alias_kind":"pith_short_12","alias_value":"GV5GTLRN2UM7","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"GV5GTLRN2UM72MPV","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"GV5GTLRN","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB","json":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB.json","graph_json":"https://pith.science/api/pith-number/GV5GTLRN2UM72MPV2DG7AZ6BRB/graph.json","events_json":"https://pith.science/api/pith-number/GV5GTLRN2UM72MPV2DG7AZ6BRB/events.json","paper":"https://pith.science/paper/GV5GTLRN"},"agent_actions":{"view_html":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB","download_json":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB.json","view_paper":"https://pith.science/paper/GV5GTLRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.06491&json=true","fetch_graph":"https://pith.science/api/pith-number/GV5GTLRN2UM72MPV2DG7AZ6BRB/graph.json","fetch_events":"https://pith.science/api/pith-number/GV5GTLRN2UM72MPV2DG7AZ6BRB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB/action/storage_attestation","attest_author":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB/action/author_attestation","sign_citation":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB/action/citation_signature","submit_replication":"https://pith.science/pith/GV5GTLRN2UM72MPV2DG7AZ6BRB/action/replication_record"}},"created_at":"2026-05-17T23:48:39.039214+00:00","updated_at":"2026-05-17T23:48:39.039214+00:00"}