%PDF-1.3 1 0 obj << /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R ] /Type /Pages /Count 9 >> endobj 2 0 obj << /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Publisher (Curran Associates\054 Inc\056) /Language (en\055US) /Created (2015) /EventType (Poster) /Description-Abstract (The robust principal component analysis \050RPCA\051 problem seeks to separate low\055rank trends from sparse outlierswithin a data matrix\054 that is\054 to approximate a \044n\134times d\044 matrix \044D\044 as the sum of a low\055rank matrix \044L\044 and a sparse matrix \044S\044\056We examine the robust principal component analysis \050RPCA\051 problem under data compression\054 wherethe data \044Y\044 is approximately given by \044\050L \053 S\051\134cdot C\044\054 that is\054 a low\055rank \044\053\044 sparse data matrix that has been compressed to size \044n\134times m\044 \050with \044m\044 substantially smaller than the original dimension \044d\044\051 via multiplication witha compression matrix \044C\044\056 We give a convex program for recovering the sparse component \044S\044 along with the compressed low\055rank component \044L\134cdot C\044\054 along with upper bounds on the error of this reconstructionthat scales naturally with the compression dimension \044m\044 and coincides with existing results for the uncompressedsetting \044m\075d\044\056 Our results can also handle error introduced through additive noise or through missing data\056The scaling of dimension\054 compression\054 and signal complexity in our theoretical results is verified empirically through simulations\054 and we also apply our method to a data set measuring chlorine concentration acrossa network of sensors\054 to test its performance in practice\056) /Producer (PyPDF2) /Title (Robust PCA with compressed data) /Date (2015) /ModDate (D\07220151218142250\05508\04700\047) /Published (2015) /Type (Conference Proceedings) /firstpage (1927) /Book (Advances in Neural Information Processing Systems 28) /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) /Editors (C\056 Cortes and N\056D\056 Lawrence and D\056D\056 Lee and M\056 Sugiyama and R\056 Garnett and R\056 Garnett) /Author (Wooseok Ha\054 Rina Foygel Barber) /lastpage (1935) >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R >> endobj 4 0 obj << /Parent 1 0 R /Contents 13 0 R /Type /Page /Resources 14 0 R /MediaBox [ 0 0 612 792 ] >> endobj 5 0 obj << /Parent 1 0 R /Contents 77 0 R /Type /Page /Resources 78 0 R /MediaBox [ 0 0 612 792 ] >> endobj 6 0 obj << /Parent 1 0 R /Contents 111 0 R /Type /Page /Resources 112 0 R /MediaBox [ 0 0 612 792 ] >> endobj 7 0 obj << /Parent 1 0 R /Contents 142 0 R /Type /Page /Resources 143 0 R /MediaBox [ 0 0 612 792 ] >> endobj 8 0 obj << /Parent 1 0 R /Contents 156 0 R /Type /Page /Resources 157 0 R /MediaBox [ 0 0 612 792 ] >> endobj 9 0 obj << /Parent 1 0 R /Contents 183 0 R /Type /Page /Resources 184 0 R /MediaBox [ 0 0 612 792 ] >> endobj 10 0 obj << /Parent 1 0 R /Contents 240 0 R /Type /Page /Resources 241 0 R /MediaBox [ 0 0 612 792 ] >> endobj 11 0 obj << /Parent 1 0 R /Contents 274 0 R /Type /Page /Resources 275 0 R /MediaBox [ 0 0 612 792 ] >> endobj 12 0 obj << /Parent 1 0 R /Contents 296 0 R /Type /Page /Resources 297 0 R /MediaBox [ 0 0 612 792 ] >> endobj 13 0 obj << /Length 4283 /Filter /FlateDecode >> stream xZnHr}_(آI&ݙ1v1<3`=T-q!Z ɞ[2=(+Ȉ+H/ 4K$"(O]S2zq;/ʃ8J/ğkGyDyeYeyh}{7mMiOmݍ_}cu͝AN)!JtQo{ށ ,mbtE&0o6ym/?M*QX5ǖƱ/pP9~q˷]뫧f[=/eAe$(2O[]+N7dLe;&Wp|^X\ze0Y=A\ٜ߱I S{? w6QHc0ێPesQpj!_aq@PIqv [ AyDix,KnP73{}]2=BڸUWeAGgU Ǯ>
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