论文标题
高斯压缩流：原理和初步结果
Gaussian Compression Stream: Principle and Preliminary Results
论文作者
论文摘要
随机预测成为处理大数据的流行工具。特别是，当应用于非负矩阵分解（NMF）时，表明结构化的随机投影比基于高斯压缩的经典策略要高得多。但是，它们仍然昂贵，并且可能无法从最近的快速随机投影技术中受益。在本文中，我们研究了结构化RAN-RAN-HAN投影命名为高斯压缩流的替代方法（I）仅基于高斯压缩，（ii）可以从上述快速技术中受益，并且（III）被证明非常适合NMF。
Random projections became popular tools to process big data. In particular, when applied to Nonnegative Matrix Factorization (NMF), it was shown that structured random projections were far more efficient than classical strategies based on Gaussian compression. However, they remain costly and might not fully benefit from recent fast random projection techniques. In this paper, we thus investigate an alternative to structured ran-om projections-named Gaussian compression stream-which (i) is based on Gaussian compressions only, (ii) can benefit from the above fast techniques, and (iii) is shown to be well-suited to NMF.
