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Systems and methods for low-rank matrix approximation

机译:低秩矩阵逼近的系统和方法

摘要

Systems and methods may identify at least a first set of right singular vectors and a first set of singular values of a subset of the matrix, reduce the subset by an amount of energy of a selected data entry of the subset based on the first set of right singular vectors and the first set of singular values, incorporate a new data entry from the matrix into the subset, update the first set of right singular vectors and the first set of singular values of the subset based on the new data entry by a singular value decomposition (SVD) update, and generate the low-rank approximation of the matrix based on the updated first set of right singular vectors and the updated first set of singular values.
机译:系统和方法可以识别矩阵的子集的至少第一组右奇异矢量和第一组奇异值,基于该子集的第一组将子集减少子集的选定数据条目的能量的量。右奇异矢量和第一组奇异值,将来自矩阵的新数据项合并到子集中,基于新数据输入以奇异值更新子集的第一组右奇异矢量和第一组奇异值值分解(SVD)更新,并根据更新后的第一组奇异矢量和更新后的第一组奇异值生成矩阵的低秩近似。

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