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Adaptive iterative hard thresholding for low-rank matrix recovery and rank-one measurements
Affiliation:1. School of Mathematics, Hangzhou Normal University, Hangzhou, 311121, China;2. School of Data Science, Zhejiang University of Finance & Economics, Hangzhou, 310018, China;1. KAIST, School of Computing, Daejeon, Republic of Korea;2. LIX, CNRS, École Polytechnique, Institute Polytechnique de Paris, France;1. Department of Mathematical and Statistical Sciences, University of Alberta Edmonton, Alberta T6G 2G1, Canada;2. Department of Mathematics, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada;3. Department of Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK;4. University of South Carolina, 1523 Greene St., Columbia SC, 29208, USA;5. Moscow Center for Fundamental and Applied Mathematics, Russian Federation;6. Steklov Institute of Mathematics, Russian Federation;7. Lomonosov Moscow State University, Russian Federation;8. Centre de Recerca Matemàtica, Campus de Bellaterra, Edifici C, 08193 Bellaterra (Barcelona), Spain;9. ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain;10. Universitat Autònoma de Barcelona, Spain
Abstract:
Keywords:Rank-one projection  Iterative hard thresholding  Restricted isometry property
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