{"paper":{"title":"What to Expect When You Are Expecting on the Grassmannian","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Armin Eftekhari, Laura Balzano, Michael B. Wakin","submitted_at":"2016-11-22T09:40:05Z","abstract_excerpt":"Consider an incoming sequence of vectors, all belonging to an unknown subspace $\\operatorname{S}$, and each with many missing entries. In order to estimate $\\operatorname{S}$, it is common to partition the data into blocks and iteratively update the estimate of $\\operatorname{S}$ with each new incoming measurement block.\n  In this paper, we investigate a rather basic question: Is it possible to identify $\\operatorname{S}$ by averaging the column span of the partially observed incoming measurement blocks on the Grassmannian?\n  We show that in general the span of the incoming blocks is in fact a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07216","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"}