{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:F4M7FZCKUD2A65GIJY554W6ZWR","short_pith_number":"pith:F4M7FZCK","canonical_record":{"source":{"id":"1507.01673","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2015-07-07T04:36:19Z","cross_cats_sorted":[],"title_canon_sha256":"84a12704eb215ac44a6af0c4313e96c23648c7bbf820202ae1aee6ac74800226","abstract_canon_sha256":"f797151121e00b2ae5d3b4b441cf6471d8837d6ab6e53f1302e30416893a259f"},"schema_version":"1.0"},"canonical_sha256":"2f19f2e44aa0f40f74c84e3bde5bd9b472cb67fdc0168b598c14e2115294af60","source":{"kind":"arxiv","id":"1507.01673","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.01673","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"1507.01673v2","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.01673","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"F4M7FZCKUD2A","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F4M7FZCKUD2A65GI","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F4M7FZCK","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:F4M7FZCKUD2A65GIJY554W6ZWR","target":"record","payload":{"canonical_record":{"source":{"id":"1507.01673","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2015-07-07T04:36:19Z","cross_cats_sorted":[],"title_canon_sha256":"84a12704eb215ac44a6af0c4313e96c23648c7bbf820202ae1aee6ac74800226","abstract_canon_sha256":"f797151121e00b2ae5d3b4b441cf6471d8837d6ab6e53f1302e30416893a259f"},"schema_version":"1.0"},"canonical_sha256":"2f19f2e44aa0f40f74c84e3bde5bd9b472cb67fdc0168b598c14e2115294af60","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:22.215367Z","signature_b64":"615oMs8MjRrqNHe9J9sDo8mnsDdFDfzS6WHf71sNb4d1UbE/syBfv0TxGYsjG6UxW5HH8u8n2jz1yO+AtQF9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f19f2e44aa0f40f74c84e3bde5bd9b472cb67fdc0168b598c14e2115294af60","last_reissued_at":"2026-05-18T01:20:22.214769Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:22.214769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.01673","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UWyFg588nilJ3a9h9WkTdRmje32tAd6R+1HbkcERTJMByDTDg35wz0vyxXYsSo+hhTZ+mWoQXrXaQNu2ImqHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:31:22.837414Z"},"content_sha256":"4c91dacb8197f255e592b48f08d8c4464cb79b6f8cc13c495ee9bbb707d0a2cf","schema_version":"1.0","event_id":"sha256:4c91dacb8197f255e592b48f08d8c4464cb79b6f8cc13c495ee9bbb707d0a2cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:F4M7FZCKUD2A65GIJY554W6ZWR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SLRMA: Sparse Low-Rank Matrix Approximation for Data Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann, Ying He","submitted_at":"2015-07-07T04:36:19Z","abstract_excerpt":"Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse low-rank matrix approximation (SLRMA), an effective computational tool for data compression. SLRMA extends the conventional LRMA by exploring both the intra- and inter-coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01673","kind":"arxiv","version":2},"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":"2026-05-18T01:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nW0UfKlkGPr2eCVDW4S2q38IaSBiWl+Xja+bRF5A3466pCOfo3z/jq18JHnHN4rmU94Z/sNPvdmh78FsgtPWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:31:22.838628Z"},"content_sha256":"d02df45a4823d489b9bd116c249698e07863cd603a16de2a307142e9423c710c","schema_version":"1.0","event_id":"sha256:d02df45a4823d489b9bd116c249698e07863cd603a16de2a307142e9423c710c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F4M7FZCKUD2A65GIJY554W6ZWR/bundle.json","state_url":"https://pith.science/pith/F4M7FZCKUD2A65GIJY554W6ZWR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F4M7FZCKUD2A65GIJY554W6ZWR/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":"2026-06-07T17:31:22Z","links":{"resolver":"https://pith.science/pith/F4M7FZCKUD2A65GIJY554W6ZWR","bundle":"https://pith.science/pith/F4M7FZCKUD2A65GIJY554W6ZWR/bundle.json","state":"https://pith.science/pith/F4M7FZCKUD2A65GIJY554W6ZWR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F4M7FZCKUD2A65GIJY554W6ZWR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:F4M7FZCKUD2A65GIJY554W6ZWR","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":"f797151121e00b2ae5d3b4b441cf6471d8837d6ab6e53f1302e30416893a259f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2015-07-07T04:36:19Z","title_canon_sha256":"84a12704eb215ac44a6af0c4313e96c23648c7bbf820202ae1aee6ac74800226"},"schema_version":"1.0","source":{"id":"1507.01673","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.01673","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"1507.01673v2","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.01673","created_at":"2026-05-18T01:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"F4M7FZCKUD2A","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F4M7FZCKUD2A65GI","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F4M7FZCK","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:d02df45a4823d489b9bd116c249698e07863cd603a16de2a307142e9423c710c","target":"graph","created_at":"2026-05-18T01:20:22Z","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":"Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse low-rank matrix approximation (SLRMA), an effective computational tool for data compression. SLRMA extends the conventional LRMA by exploring both the intra- and inter-coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product ","authors_text":"Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann, Ying He","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2015-07-07T04:36:19Z","title":"SLRMA: Sparse Low-Rank Matrix Approximation for Data Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01673","kind":"arxiv","version":2},"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":"sha256:4c91dacb8197f255e592b48f08d8c4464cb79b6f8cc13c495ee9bbb707d0a2cf","target":"record","created_at":"2026-05-18T01:20:22Z","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":"f797151121e00b2ae5d3b4b441cf6471d8837d6ab6e53f1302e30416893a259f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2015-07-07T04:36:19Z","title_canon_sha256":"84a12704eb215ac44a6af0c4313e96c23648c7bbf820202ae1aee6ac74800226"},"schema_version":"1.0","source":{"id":"1507.01673","kind":"arxiv","version":2}},"canonical_sha256":"2f19f2e44aa0f40f74c84e3bde5bd9b472cb67fdc0168b598c14e2115294af60","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f19f2e44aa0f40f74c84e3bde5bd9b472cb67fdc0168b598c14e2115294af60","first_computed_at":"2026-05-18T01:20:22.214769Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:22.214769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"615oMs8MjRrqNHe9J9sDo8mnsDdFDfzS6WHf71sNb4d1UbE/syBfv0TxGYsjG6UxW5HH8u8n2jz1yO+AtQF9BA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:22.215367Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.01673","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c91dacb8197f255e592b48f08d8c4464cb79b6f8cc13c495ee9bbb707d0a2cf","sha256:d02df45a4823d489b9bd116c249698e07863cd603a16de2a307142e9423c710c"],"state_sha256":"3d7a305ef5b6b0e382a839c89572376a127468e665e09e7cfaa27fb60c246a26"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u2wH1gtNIyNPJ8G2j6OOknEdgkVt+RooFkq88imFYRpjMjKHIGn3VQSLrg5othFjCGMV+R/PdG9diE1zUBmsDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:31:22.842254Z","bundle_sha256":"465a16e4a7efb53be497b7b9e2dee86975aacf11e24a3f341d347ef7fbba307d"}}