{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:JWTHCC6BSNWYUNLQZL36OBKJ63","short_pith_number":"pith:JWTHCC6B","schema_version":"1.0","canonical_sha256":"4da6710bc1936d8a3570caf7e70549f6dc6109eacd61723c9a369a7bb350f4c1","source":{"kind":"arxiv","id":"1406.2206","version":1},"attestation_state":"computed","paper":{"title":"Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Aarti Singh, Larry Wasserman, Martin Azizyan","submitted_at":"2014-06-09T14:57:16Z","abstract_excerpt":"We consider the problem of clustering data points in high dimensions, i.e. when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with non-spherical Gaussian components, where the clusters are distinguished by only a few relevant dimensions. The method we propose is a combination of a recent approach for learning parameters of a Gaussian mixture model and sparse linear discriminant analysis (LDA). In addition to cluster assignments, the method returns an estimate of the set of features relevant for clustering. "},"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":"1406.2206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-06-09T14:57:16Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"ecb679840742660d92c3fbfad98e7a41c76969746d08b68169092273c92e3a24","abstract_canon_sha256":"387ef811f83dca5eb247e6e247ab378ed67abb5b266a65e9c123f7f3ff88033f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:50:11.282716Z","signature_b64":"F08/OFiJw/AjpeETnbzUcefdK31UYjiRbeoJU/Sl0M+y6Dr9P+vhw/buqpVqsM0ZI+TMFgxyRX/wtBkKupBhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4da6710bc1936d8a3570caf7e70549f6dc6109eacd61723c9a369a7bb350f4c1","last_reissued_at":"2026-05-18T02:50:11.282193Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:50:11.282193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Aarti Singh, Larry Wasserman, Martin Azizyan","submitted_at":"2014-06-09T14:57:16Z","abstract_excerpt":"We consider the problem of clustering data points in high dimensions, i.e. when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with non-spherical Gaussian components, where the clusters are distinguished by only a few relevant dimensions. The method we propose is a combination of a recent approach for learning parameters of a Gaussian mixture model and sparse linear discriminant analysis (LDA). In addition to cluster assignments, the method returns an estimate of the set of features relevant for clustering. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.2206","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1406.2206","created_at":"2026-05-18T02:50:11.282284+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.2206v1","created_at":"2026-05-18T02:50:11.282284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.2206","created_at":"2026-05-18T02:50:11.282284+00:00"},{"alias_kind":"pith_short_12","alias_value":"JWTHCC6BSNWY","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"JWTHCC6BSNWYUNLQ","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"JWTHCC6B","created_at":"2026-05-18T12:28:35.611951+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/JWTHCC6BSNWYUNLQZL36OBKJ63","json":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63.json","graph_json":"https://pith.science/api/pith-number/JWTHCC6BSNWYUNLQZL36OBKJ63/graph.json","events_json":"https://pith.science/api/pith-number/JWTHCC6BSNWYUNLQZL36OBKJ63/events.json","paper":"https://pith.science/paper/JWTHCC6B"},"agent_actions":{"view_html":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63","download_json":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63.json","view_paper":"https://pith.science/paper/JWTHCC6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.2206&json=true","fetch_graph":"https://pith.science/api/pith-number/JWTHCC6BSNWYUNLQZL36OBKJ63/graph.json","fetch_events":"https://pith.science/api/pith-number/JWTHCC6BSNWYUNLQZL36OBKJ63/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63/action/storage_attestation","attest_author":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63/action/author_attestation","sign_citation":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63/action/citation_signature","submit_replication":"https://pith.science/pith/JWTHCC6BSNWYUNLQZL36OBKJ63/action/replication_record"}},"created_at":"2026-05-18T02:50:11.282284+00:00","updated_at":"2026-05-18T02:50:11.282284+00:00"}