SketchyCoreSVD:从数据矩阵的随机子采样的SketchySVD。

Proceedings. IEEE International Congress on Big Data Pub Date : 2019-12-01 Epub Date: 2020-02-24 DOI:10.1109/bigdata47090.2019.9006345

Chandrajit Bajaj, Yi Wang, Tianming Wang

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引用次数: 5

摘要

我们提出了一种称为SketchyCoreSVD的方法，通过仅从其子采样的列和行构建随机草图来计算数据矩阵的近最优秩r SVD。我们在非相干假设下提供了理论保证，并在各种大型静态和时变数据集上验证了我们的SketchyCoreSVD方法的性能。

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SketchyCoreSVD: SketchySVD from Random Subsampling of the Data Matrix.

We present a method called SketchyCoreSVD to compute the near-optimal rank r SVD of a data matrix by building random sketches only from its subsampled columns and rows. We provide theoretical guarantees under incoherence assumptions, and validate the performance of our SketchyCoreSVD method on various large static and time-varying datasets.

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Proceedings. IEEE International Congress on Big Data

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