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One of the important approaches of compressive sensing is trace norm minimization, which can minimize the rank of the data matrix under some conditions. For example, in collaborative filtering, we are given a small set of observed item ratings of some users and we want to predict the missing values in the rating matrix. It is assumed that the users' ratings are affected by only a few factors and the resulting rating matrix should be of low rank. In this paper, we analyze the issues related to trace norm minimization and find an unexpected result that trace norm minimization often does not work as well as expected.<\/jats:p>","DOI":"10.1145\/1964897.1964902","type":"journal-article","created":{"date-parts":[[2011,4,1]],"date-time":"2011-04-01T15:54:25Z","timestamp":1301673265000},"page":"16-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Limitations of matrix completion via trace norm minimization"],"prefix":"10.1145","volume":"12","author":[{"given":"Xiaoxiao","family":"Shi","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}]}],"member":"320","published-online":{"date-parts":[[2011,3,31]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273499"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5040-8"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/1390681.1390716"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/1958473.1958487"},{"key":"e_1_2_1_7_1","unstructured":"M. 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