{"paper":{"title":"Sketching for Simultaneously Sparse and Low-Rank Covariance Matrices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.NA","math.ST","stat.TH"],"primary_cat":"cs.IT","authors_text":"Justin Romberg, Sohail Bahmani","submitted_at":"2015-10-06T17:13:00Z","abstract_excerpt":"We introduce a technique for estimating a structured covariance matrix from observations of a random vector which have been sketched. Each observed random vector $\\boldsymbol{x}_t$ is reduced to a single number by taking its inner product against one of a number of pre-selected vector $\\boldsymbol{a}_\\ell$. These observations are used to form estimates of linear observations of the covariance matrix $\\boldsymbol{\\varSigma}$, which is assumed to be simultaneously sparse and low-rank. We show that if the sketching vectors $\\boldsymbol{a}_\\ell$ have a special structure, then we can use straightfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.01670","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"}