Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"Nuclear norm minimization"”.
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Essays in Problems in Sequential Decisions and Large-Scale Randomized Algorithms
… a central limit theorem (CLT) with a different normalization than Renyi's classical CLT, and in the constricted case we find convergence in distribution to an unbounded random variable. In the second part of this dissertation, we put forward two large-scale randomized algorithms. We propose a …
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Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
… its matrix extensions such as the matrix rank minimization and robust principal component analysis problems. Compressed sensing enables one to recover a signal or image with fewer observations than the "length" of the signal or image, and thus provides potential breakthroughs in applications …
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LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS
… many data analysis tasks. Since the general rank minimization problem is computationally NP-hard, the convex relaxation of original problem is often solved. One popular heuristic method is to use the nuclear norm to approximate the rank of a matrix. Despite the success of nuclear norm minimization …
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Advanced imaging via multiplexed sensing and compressive sensing
… sampling. In addition, we propose a new L1-norm based total-variation measure TVL1, which enforces the sparsity and the directional continuity in the partial gradient domain. Theoretical and experimental results show that this new TVL1 achieves higher recovery accuracy than the previous TV …
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Collaborative ranking from ordinal data
Personalized recommendation systems have to predict preferences of a user for items that have not seen by the user. For cardinal (ratings) data, personalized preference prediction has been efficiently solved over the past few years using matrix factorization related techniques. Recent studies have …