{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:3ZS5JCXM7FQPOCUSC3SPU67V66","short_pith_number":"pith:3ZS5JCXM","canonical_record":{"source":{"id":"1806.00650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-02T15:11:38Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"934a50e125b569e63da98daf0f789b9f5f8ecafe1dc7bfbdaf334d078f4b7bbc","abstract_canon_sha256":"fbf93f26e7f61e8bd2390cbffc864d012bf2efd9bc2a7293e6cdce0881766b76"},"schema_version":"1.0"},"canonical_sha256":"de65d48aecf960f70a9216e4fa7bf5f7beda5c575e2b8de9c17b88c0bf3878b9","source":{"kind":"arxiv","id":"1806.00650","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00650","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00650v1","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00650","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"pith_short_12","alias_value":"3ZS5JCXM7FQP","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"3ZS5JCXM7FQPOCUS","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"3ZS5JCXM","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:3ZS5JCXM7FQPOCUSC3SPU67V66","target":"record","payload":{"canonical_record":{"source":{"id":"1806.00650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-02T15:11:38Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"934a50e125b569e63da98daf0f789b9f5f8ecafe1dc7bfbdaf334d078f4b7bbc","abstract_canon_sha256":"fbf93f26e7f61e8bd2390cbffc864d012bf2efd9bc2a7293e6cdce0881766b76"},"schema_version":"1.0"},"canonical_sha256":"de65d48aecf960f70a9216e4fa7bf5f7beda5c575e2b8de9c17b88c0bf3878b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:18.825225Z","signature_b64":"64P1DcnPK1lVzgMpzH81Mgb1tGXabu/X92K8JzeXGdJRR+Fuz7WJv+vx/wVMof0prUZSuLh4jdscBbcoemmSDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de65d48aecf960f70a9216e4fa7bf5f7beda5c575e2b8de9c17b88c0bf3878b9","last_reissued_at":"2026-05-18T00:14:18.824780Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:18.824780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.00650","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":"2026-05-18T00:14:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8B59glEsCK73hZao8/umejWa56hhLVqqQv8Oe5xqoDJu1LPYSjL2Xhd1sQ6e86+YCXj3W8eS4pRa9aWzY820Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T06:06:22.815847Z"},"content_sha256":"c700bf9e64fb9dbd3c26b7aac91fa6e6dc2d61229b2cbad96252c241c5fa3d96","schema_version":"1.0","event_id":"sha256:c700bf9e64fb9dbd3c26b7aac91fa6e6dc2d61229b2cbad96252c241c5fa3d96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:3ZS5JCXM7FQPOCUSC3SPU67V66","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"stat.ML","authors_text":"Sheetal Kalyani, Sreejith Kallummil","submitted_at":"2018-06-02T15:11:38Z","abstract_excerpt":"Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \\textit{a priori} knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known \\textit{a priori} and are very difficult to estimate. In this paper, we present a novel technique called residual ratio thresholding (RRT) to operate OMP without any \\textit{a priori} knowledge of sparsity and noise statistics and establish finite sample and large sample support recovery gu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00650","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":"2026-05-18T00:14:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wPFo+XRA2BHvFYj5tPkt0jrLN7sYIH7gEfthkujTCghMf1do6kQri6g6OQuTwNYGrBeoGw3F9rZ/wH/9eB4uAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T06:06:22.816213Z"},"content_sha256":"aeef81ffe56c878d14f1691028a13d101eee51034be0b5fc8facd012e1aefc38","schema_version":"1.0","event_id":"sha256:aeef81ffe56c878d14f1691028a13d101eee51034be0b5fc8facd012e1aefc38"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/bundle.json","state_url":"https://pith.science/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/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":"2026-06-20T06:06:22Z","links":{"resolver":"https://pith.science/pith/3ZS5JCXM7FQPOCUSC3SPU67V66","bundle":"https://pith.science/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/bundle.json","state":"https://pith.science/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3ZS5JCXM7FQPOCUSC3SPU67V66/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3ZS5JCXM7FQPOCUSC3SPU67V66","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":"fbf93f26e7f61e8bd2390cbffc864d012bf2efd9bc2a7293e6cdce0881766b76","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-02T15:11:38Z","title_canon_sha256":"934a50e125b569e63da98daf0f789b9f5f8ecafe1dc7bfbdaf334d078f4b7bbc"},"schema_version":"1.0","source":{"id":"1806.00650","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00650","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00650v1","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00650","created_at":"2026-05-18T00:14:18Z"},{"alias_kind":"pith_short_12","alias_value":"3ZS5JCXM7FQP","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"3ZS5JCXM7FQPOCUS","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"3ZS5JCXM","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:aeef81ffe56c878d14f1691028a13d101eee51034be0b5fc8facd012e1aefc38","target":"graph","created_at":"2026-05-18T00:14:18Z","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":"Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \\textit{a priori} knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known \\textit{a priori} and are very difficult to estimate. In this paper, we present a novel technique called residual ratio thresholding (RRT) to operate OMP without any \\textit{a priori} knowledge of sparsity and noise statistics and establish finite sample and large sample support recovery gu","authors_text":"Sheetal Kalyani, Sreejith Kallummil","cross_cats":["cs.IT","cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-02T15:11:38Z","title":"Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00650","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:c700bf9e64fb9dbd3c26b7aac91fa6e6dc2d61229b2cbad96252c241c5fa3d96","target":"record","created_at":"2026-05-18T00:14:18Z","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":"fbf93f26e7f61e8bd2390cbffc864d012bf2efd9bc2a7293e6cdce0881766b76","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-02T15:11:38Z","title_canon_sha256":"934a50e125b569e63da98daf0f789b9f5f8ecafe1dc7bfbdaf334d078f4b7bbc"},"schema_version":"1.0","source":{"id":"1806.00650","kind":"arxiv","version":1}},"canonical_sha256":"de65d48aecf960f70a9216e4fa7bf5f7beda5c575e2b8de9c17b88c0bf3878b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de65d48aecf960f70a9216e4fa7bf5f7beda5c575e2b8de9c17b88c0bf3878b9","first_computed_at":"2026-05-18T00:14:18.824780Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:18.824780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"64P1DcnPK1lVzgMpzH81Mgb1tGXabu/X92K8JzeXGdJRR+Fuz7WJv+vx/wVMof0prUZSuLh4jdscBbcoemmSDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:18.825225Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00650","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c700bf9e64fb9dbd3c26b7aac91fa6e6dc2d61229b2cbad96252c241c5fa3d96","sha256:aeef81ffe56c878d14f1691028a13d101eee51034be0b5fc8facd012e1aefc38"],"state_sha256":"cb8cd96c751cc7bd2cdac6f6ffca6ef61f347d8c5ca384efd6df0c30b65eb832"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AOt9FQU1alvvfvQsRRc9eBJBEflg92uaAwgcQlbFptnnwrU4ft6zVwDSXDl6wsCiaGoGbH39sivWy4YqG5yyBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T06:06:22.818225Z","bundle_sha256":"9cbf13d4c00c4d75ce1f849b8fc19dbe405a01e65db2aa7443b88981379974be"}}