{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:CWZDJJRDTE4R52YCUUNAK3T3MW","short_pith_number":"pith:CWZDJJRD","canonical_record":{"source":{"id":"1201.3599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-01-17T19:14:33Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"ef77c4a396b64aad1aa2112907a26cdc359763a87b4568859cf783f6e5e0e6dc","abstract_canon_sha256":"9f0c6df9e570d2ad05d85a23f22e0ce2aff26fda22d1640d1b8e1ea36a82c141"},"schema_version":"1.0"},"canonical_sha256":"15b234a62399391eeb02a51a056e7b65b4332d3c6d709e1e2a668f7220f53c64","source":{"kind":"arxiv","id":"1201.3599","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.3599","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"1201.3599v1","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.3599","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"CWZDJJRDTE4R","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CWZDJJRDTE4R52YC","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CWZDJJRD","created_at":"2026-05-18T12:27:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:CWZDJJRDTE4R52YCUUNAK3T3MW","target":"record","payload":{"canonical_record":{"source":{"id":"1201.3599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-01-17T19:14:33Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"ef77c4a396b64aad1aa2112907a26cdc359763a87b4568859cf783f6e5e0e6dc","abstract_canon_sha256":"9f0c6df9e570d2ad05d85a23f22e0ce2aff26fda22d1640d1b8e1ea36a82c141"},"schema_version":"1.0"},"canonical_sha256":"15b234a62399391eeb02a51a056e7b65b4332d3c6d709e1e2a668f7220f53c64","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:58:47.979489Z","signature_b64":"KHy9k2aiX4dK3mAksfC5vvFg7dwYPRrB8xAh1YfBW9NaYwHpT7NFEE/dyg6ih6RTmBMkxlAJ4p3+EbhhgpLAAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15b234a62399391eeb02a51a056e7b65b4332d3c6d709e1e2a668f7220f53c64","last_reissued_at":"2026-05-18T01:58:47.978916Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:58:47.978916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1201.3599","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":"2026-05-18T01:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iLmRsGD7iiK/VnKkN743T8akOGLL7Tylu0j71gKAZJEr3g6+KzLZYgOPBZwCAvlXpQ3NuLS+2baYG8t/LL+oCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T04:31:24.494695Z"},"content_sha256":"2616203e4369deb6192e4101f6d9963525eccf32cebdf87be3b34ada63c4c808","schema_version":"1.0","event_id":"sha256:2616203e4369deb6192e4101f6d9963525eccf32cebdf87be3b34ada63c4c808"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:CWZDJJRDTE4R52YCUUNAK3T3MW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Covariance Eigenvector Sparsity for Compression and Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"stat.AP","authors_text":"Georgios B. Giannakis, Ioannis D. Schizas","submitted_at":"2012-01-17T19:14:33Z","abstract_excerpt":"Sparsity in the eigenvectors of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present in many practical compression schemes such as transform codecs, are designed to capitalize on this form of sparsity and achieve improved reconstruction performance compared to existing sparsity-agnostic codecs. Using training data that may be noisy a novel sparsity-aware linear DR scheme is developed to fully exploit sparsity in the covariance eigenvectors and form noise-resilient estimates of the principal covarianc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.3599","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":"2026-05-18T01:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q5cIfZ0tC6rrVTpzyIdkKKAUqPNGLPrGny9AKnUIWkN5Ss9Mor5qYnzQPERdjK+o6O/SWOAd7GM7G1eob9o1Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T04:31:24.495040Z"},"content_sha256":"9005df55ece297464397e6cfacc55f0a1d5600bb8a637e15d549286986dbd399","schema_version":"1.0","event_id":"sha256:9005df55ece297464397e6cfacc55f0a1d5600bb8a637e15d549286986dbd399"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/bundle.json","state_url":"https://pith.science/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/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":"2026-06-04T04:31:24Z","links":{"resolver":"https://pith.science/pith/CWZDJJRDTE4R52YCUUNAK3T3MW","bundle":"https://pith.science/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/bundle.json","state":"https://pith.science/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CWZDJJRDTE4R52YCUUNAK3T3MW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:CWZDJJRDTE4R52YCUUNAK3T3MW","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":"9f0c6df9e570d2ad05d85a23f22e0ce2aff26fda22d1640d1b8e1ea36a82c141","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-01-17T19:14:33Z","title_canon_sha256":"ef77c4a396b64aad1aa2112907a26cdc359763a87b4568859cf783f6e5e0e6dc"},"schema_version":"1.0","source":{"id":"1201.3599","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.3599","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"1201.3599v1","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.3599","created_at":"2026-05-18T01:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"CWZDJJRDTE4R","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CWZDJJRDTE4R52YC","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CWZDJJRD","created_at":"2026-05-18T12:27:01Z"}],"graph_snapshots":[{"event_id":"sha256:9005df55ece297464397e6cfacc55f0a1d5600bb8a637e15d549286986dbd399","target":"graph","created_at":"2026-05-18T01:58:47Z","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":"Sparsity in the eigenvectors of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present in many practical compression schemes such as transform codecs, are designed to capitalize on this form of sparsity and achieve improved reconstruction performance compared to existing sparsity-agnostic codecs. Using training data that may be noisy a novel sparsity-aware linear DR scheme is developed to fully exploit sparsity in the covariance eigenvectors and form noise-resilient estimates of the principal covarianc","authors_text":"Georgios B. Giannakis, Ioannis D. Schizas","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-01-17T19:14:33Z","title":"Covariance Eigenvector Sparsity for Compression and Denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.3599","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":"sha256:2616203e4369deb6192e4101f6d9963525eccf32cebdf87be3b34ada63c4c808","target":"record","created_at":"2026-05-18T01:58:47Z","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":"9f0c6df9e570d2ad05d85a23f22e0ce2aff26fda22d1640d1b8e1ea36a82c141","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-01-17T19:14:33Z","title_canon_sha256":"ef77c4a396b64aad1aa2112907a26cdc359763a87b4568859cf783f6e5e0e6dc"},"schema_version":"1.0","source":{"id":"1201.3599","kind":"arxiv","version":1}},"canonical_sha256":"15b234a62399391eeb02a51a056e7b65b4332d3c6d709e1e2a668f7220f53c64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15b234a62399391eeb02a51a056e7b65b4332d3c6d709e1e2a668f7220f53c64","first_computed_at":"2026-05-18T01:58:47.978916Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:58:47.978916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KHy9k2aiX4dK3mAksfC5vvFg7dwYPRrB8xAh1YfBW9NaYwHpT7NFEE/dyg6ih6RTmBMkxlAJ4p3+EbhhgpLAAg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:58:47.979489Z","signed_message":"canonical_sha256_bytes"},"source_id":"1201.3599","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2616203e4369deb6192e4101f6d9963525eccf32cebdf87be3b34ada63c4c808","sha256:9005df55ece297464397e6cfacc55f0a1d5600bb8a637e15d549286986dbd399"],"state_sha256":"8a37052ccdb86d6c0eff8d4eb2f38e440f7e4c7b9718e5fc12f986c1470ebcc3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kdf3+5dYe2iS+5FgvasxaxW+wNOnRaO3Q4KT1yiYAmyOKrDqImyiTv/KADJNRfqhCU8oNn6Ca2yMJnZlnxuBDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T04:31:24.498105Z","bundle_sha256":"7857d69ea31a25ffeddf593edc98af98ad90ab0231125b99d8b8ab4217abd940"}}