{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4HXQAIUKSZE44ZPKXUAL6ONHUQ","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":"ce92095fa735744d664a558c652b24f395f5ce1e8a8f03194132aafe253e84b4","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T02:39:40Z","title_canon_sha256":"f5f86f6d107c17e039281b3c709ebd56a47fce6d91dfa775e8f48518582387f8"},"schema_version":"1.0","source":{"id":"1908.00683","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.00683","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"arxiv_version","alias_value":"1908.00683v1","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.00683","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_12","alias_value":"4HXQAIUKSZE4","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_16","alias_value":"4HXQAIUKSZE44ZPK","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_8","alias_value":"4HXQAIUK","created_at":"2026-07-04T23:51:08Z"}],"graph_snapshots":[{"event_id":"sha256:7014cb095332ed571682b58bea879740d94d57b0c399b5cf74e35b71d147d995","target":"graph","created_at":"2026-07-04T23:51:08Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.00683/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Subspace clustering methods based on expressing each data point as a linear combination of all other points in a dataset are popular unsupervised learning techniques. However, existing methods incur high computational complexity on large-scale datasets as they require solving an expensive optimization problem and performing spectral clustering on large affinity matrices. This paper presents an efficient approach to subspace clustering by selecting a small subset of the input data called landmarks. The resulting subspace clustering method in the reduced domain runs in linear time with respect t","authors_text":"Farhad Pourkamali-Anaraki","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T02:39:40Z","title":"Large-Scale Sparse Subspace Clustering Using Landmarks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.00683","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":"sha256:67a569504350cbfc2691001a9d59f59b1e82c8b0219ad23845f1d20677fdf5bc","target":"record","created_at":"2026-07-04T23:51:08Z","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":"ce92095fa735744d664a558c652b24f395f5ce1e8a8f03194132aafe253e84b4","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T02:39:40Z","title_canon_sha256":"f5f86f6d107c17e039281b3c709ebd56a47fce6d91dfa775e8f48518582387f8"},"schema_version":"1.0","source":{"id":"1908.00683","kind":"arxiv","version":1}},"canonical_sha256":"e1ef00228a9649ce65eabd00bf39a7a424891d77fda929d1b6ea8350edf39b8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1ef00228a9649ce65eabd00bf39a7a424891d77fda929d1b6ea8350edf39b8c","first_computed_at":"2026-07-04T23:51:08.314498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:51:08.314498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wFkf/6rsm2TMcMropLTjcA+kHnN9/mPl+rXpax5XRVG0PkD6UrUqgBNdmhEuliDZyTFSe3L6Xy3W9J2Bsk9TBg==","signature_status":"signed_v1","signed_at":"2026-07-04T23:51:08.314925Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.00683","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67a569504350cbfc2691001a9d59f59b1e82c8b0219ad23845f1d20677fdf5bc","sha256:7014cb095332ed571682b58bea879740d94d57b0c399b5cf74e35b71d147d995"],"state_sha256":"8a58f52f7819cfbe4ec645bdfbe951feae1b60485b3e632007effa3bb5f0e80a"}