{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GPH35PNF5VHNFIWJOYMFKXB3BV","short_pith_number":"pith:GPH35PNF","schema_version":"1.0","canonical_sha256":"33cfbebda5ed4ed2a2c97618555c3b0d671001d8624260da41d2d0c5bb85b4a3","source":{"kind":"arxiv","id":"1605.07785","version":1},"attestation_state":"computed","paper":{"title":"Geometry-aware stationary subspace analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Florian Yger, Inbal Horev, Masashi Sugiyama","submitted_at":"2016-05-25T09:11:23Z","abstract_excerpt":"In many real-world applications data exhibits non-stationarity, i.e., its distribution changes over time. One approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface (BCI) data analysis this may be done by means of stationary subspace analysis (SSA). The classic SSA method finds a matrix that projects the data onto a stationary subspace by optimizing a cost function based on a matrix divergence. In this work we present an alternative method for SSA based on a symmetrized version of this matrix divergenc"},"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":"1605.07785","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-25T09:11:23Z","cross_cats_sorted":[],"title_canon_sha256":"c1d8c5b928bbc265be42fe43f2df52e3bab77996affd52b847d88af3ada90e7f","abstract_canon_sha256":"7a4cc80ed6e1e29f0d257cbe091be810a45afec47c4f6fa03bc684f1664101ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:39.117389Z","signature_b64":"3BjNte8DmPdhUOAc+G05TJDpF0w0XVEBMKgtBGWltAbKL7Rmhzaw/RrUZOWmVackUTUAbfKO4QW/vugXS10pCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33cfbebda5ed4ed2a2c97618555c3b0d671001d8624260da41d2d0c5bb85b4a3","last_reissued_at":"2026-05-18T01:13:39.116685Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:39.116685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geometry-aware stationary subspace analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Florian Yger, Inbal Horev, Masashi Sugiyama","submitted_at":"2016-05-25T09:11:23Z","abstract_excerpt":"In many real-world applications data exhibits non-stationarity, i.e., its distribution changes over time. One approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface (BCI) data analysis this may be done by means of stationary subspace analysis (SSA). The classic SSA method finds a matrix that projects the data onto a stationary subspace by optimizing a cost function based on a matrix divergence. In this work we present an alternative method for SSA based on a symmetrized version of this matrix divergenc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.07785","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":"1605.07785","created_at":"2026-05-18T01:13:39.116793+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.07785v1","created_at":"2026-05-18T01:13:39.116793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.07785","created_at":"2026-05-18T01:13:39.116793+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPH35PNF5VHN","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPH35PNF5VHNFIWJ","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPH35PNF","created_at":"2026-05-18T12:30:19.053100+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/GPH35PNF5VHNFIWJOYMFKXB3BV","json":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV.json","graph_json":"https://pith.science/api/pith-number/GPH35PNF5VHNFIWJOYMFKXB3BV/graph.json","events_json":"https://pith.science/api/pith-number/GPH35PNF5VHNFIWJOYMFKXB3BV/events.json","paper":"https://pith.science/paper/GPH35PNF"},"agent_actions":{"view_html":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV","download_json":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV.json","view_paper":"https://pith.science/paper/GPH35PNF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.07785&json=true","fetch_graph":"https://pith.science/api/pith-number/GPH35PNF5VHNFIWJOYMFKXB3BV/graph.json","fetch_events":"https://pith.science/api/pith-number/GPH35PNF5VHNFIWJOYMFKXB3BV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV/action/storage_attestation","attest_author":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV/action/author_attestation","sign_citation":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV/action/citation_signature","submit_replication":"https://pith.science/pith/GPH35PNF5VHNFIWJOYMFKXB3BV/action/replication_record"}},"created_at":"2026-05-18T01:13:39.116793+00:00","updated_at":"2026-05-18T01:13:39.116793+00:00"}