小秩矩阵(low-rank matrix)在核方法和抽样中，可有效地减小计算开销。也可称作“低秩矩阵”。低秩矩阵rank就是指矩阵的秩啊，low-rank matrix可能是指秩比较小的矩阵
a low-rank matrix A from a corrupted data matrix D = A+E, where some entries of E may be of arbitrarily large magnitude. Recently, [3] showed that under surprisingly broad condi- tions, one can exactly recover the low-rank matrix A from D = A + E with gross but sparse errors E ...
小秩矩阵(low-rank matrix)在核方法和抽样中，可有效地减小计算开销。也可称作“低秩矩阵”。
Low Rank Matrix Optimization Problems:Back to Nonconvex RegularizationsDefeng SunDepartment of Mathematics and Risk Management InstituteNati..
In this paper, a successive low-rank matrix approximation algorithm is presented for the matrix completion (MC) based on hard thresholding method, which approximate the optimal low-rank matrix from rank-one matrix step by step. The algorithm enables the distance between the ma...
low rank matrix Suppose that we have a rank-rmatrixAof sizemxn, wherer <<min(m,n). In many engineering problems, the entries of the matrix are often corrupted by errors or noise, some of the entries could even be missing, or only a set of measurements of the matrix is accessible rat...
Low-rank matrix approximation with stability ICML 2016Abstract Low-rank matrix approximation has been widely adopted in machine learning applications with sparse data, such as recommender systems. However, the sparsity of the data, incomplete and noisy, introduces challenges to the algorithm stability -...
Low-Rank Matrix Completion 来自 Semantic Scholar 喜欢 0 阅读量: 128 作者: R Kennedy 摘要: While datasets are frequently represented as matrices, real-word data is imperfect and entries are often missing. In many cases, the data are very sparse and the matrix must be filled in before any ...
Low-rank matrix recovery problem is difficult due to its non-convex properties and it is usually solved using convex relaxation approaches. In this paper, we formulate the non-convex low-rank matrix recovery problem exactly using novel Ky Fan 2-k-norm-based models. A general difference of conv...
Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse low-rank matrix approximation (SLRMA), an effective computatio...