{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FOBRLLGTTZSTQ5P7EWECANBJSB","short_pith_number":"pith:FOBRLLGT","schema_version":"1.0","canonical_sha256":"2b8315acd39e653875ff258820342990547dcc09b5fb447bc2cebeee9d14b903","source":{"kind":"arxiv","id":"2103.04064","version":1},"attestation_state":"computed","paper":{"title":"Tensor Laplacian Regularized Low-Rank Representation for Non-uniformly Distributed Data Subspace Clustering","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eysan Mehrbani, Mohammad Hossein Kahaei, Seyed Aliasghar Beheshti","submitted_at":"2021-03-06T08:22:24Z","abstract_excerpt":"Low-Rank Representation (LRR) highly suffers from discarding the locality information of data points in subspace clustering, as it may not incorporate the data structure nonlinearity and the non-uniform distribution of observations over the ambient space. Thus, the information of the observational density is lost by the state-of-art LRR models, as they take a constant number of adjacent neighbors into account. This, as a result, degrades the subspace clustering accuracy in such situations. To cope with deficiency, in this paper, we propose to consider a hypergraph model to facilitate having a "},"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":"2103.04064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-06T08:22:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e03dd3fb34ff175419a6b9b855e480e770939c703f8ea0d56d85468b2488b943","abstract_canon_sha256":"c49769e882fb39634082000c4dd5bec2d99bc2c4b551de5458fd9bd4d1836198"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:02:52.654058Z","signature_b64":"BS+eFsq8M+y68RLiiTyNBC7/P81WR1MwLtqQ1CoytmJawJ37omVihV5EeL9HXdP0XOr2kWqgeLA1RMVpUpZJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b8315acd39e653875ff258820342990547dcc09b5fb447bc2cebeee9d14b903","last_reissued_at":"2026-07-05T04:02:52.653501Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:02:52.653501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor Laplacian Regularized Low-Rank Representation for Non-uniformly Distributed Data Subspace Clustering","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eysan Mehrbani, Mohammad Hossein Kahaei, Seyed Aliasghar Beheshti","submitted_at":"2021-03-06T08:22:24Z","abstract_excerpt":"Low-Rank Representation (LRR) highly suffers from discarding the locality information of data points in subspace clustering, as it may not incorporate the data structure nonlinearity and the non-uniform distribution of observations over the ambient space. Thus, the information of the observational density is lost by the state-of-art LRR models, as they take a constant number of adjacent neighbors into account. This, as a result, degrades the subspace clustering accuracy in such situations. To cope with deficiency, in this paper, we propose to consider a hypergraph model to facilitate having a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.04064","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.04064/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2103.04064","created_at":"2026-07-05T04:02:52.653562+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.04064v1","created_at":"2026-07-05T04:02:52.653562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.04064","created_at":"2026-07-05T04:02:52.653562+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOBRLLGTTZST","created_at":"2026-07-05T04:02:52.653562+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOBRLLGTTZSTQ5P7","created_at":"2026-07-05T04:02:52.653562+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOBRLLGT","created_at":"2026-07-05T04:02:52.653562+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/FOBRLLGTTZSTQ5P7EWECANBJSB","json":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB.json","graph_json":"https://pith.science/api/pith-number/FOBRLLGTTZSTQ5P7EWECANBJSB/graph.json","events_json":"https://pith.science/api/pith-number/FOBRLLGTTZSTQ5P7EWECANBJSB/events.json","paper":"https://pith.science/paper/FOBRLLGT"},"agent_actions":{"view_html":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB","download_json":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB.json","view_paper":"https://pith.science/paper/FOBRLLGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.04064&json=true","fetch_graph":"https://pith.science/api/pith-number/FOBRLLGTTZSTQ5P7EWECANBJSB/graph.json","fetch_events":"https://pith.science/api/pith-number/FOBRLLGTTZSTQ5P7EWECANBJSB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB/action/storage_attestation","attest_author":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB/action/author_attestation","sign_citation":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB/action/citation_signature","submit_replication":"https://pith.science/pith/FOBRLLGTTZSTQ5P7EWECANBJSB/action/replication_record"}},"created_at":"2026-07-05T04:02:52.653562+00:00","updated_at":"2026-07-05T04:02:52.653562+00:00"}