Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace
Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace
复制标题
DOI：
10.1145/3432261.3432267
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Takahiro Yano;Y. Futamura;A. Imakura;T. Sakurai
中科院分区：
文献类型：
--
作者：
Takahiro Yano;Y. Futamura;A. Imakura;T. Sakurai
简化方法被广泛用于有效地处理数据。最近，Imakura等人提出了一种新的降维方法，使用一个复杂的基于矩的子空间。他们的方法可以使用更多的特征向量比现有的矩阵迹优化为基础的方法，这解释了其较高的精度。然而，计算复杂度也高于现有的方法，特别是对于非线性内核版本。为了降低计算复杂度，我们提出了一个实用的并行实现的方法，通过引入Nyström近似。我们使用Oakforest-PACS超级计算机来评估我们实现的并行性能。
Dimensionality reduction methods are widely used for processing data efficiently. Recently Imakura et al. proposed a novel dimensionality reduction method using a complex moment-based subspace. Their method can use more eigenvectors than the existing matrix trace optimization-based methods which explains its reported higher precision. However, the computational complexity is also higher than that of the existing methods, in particular for the nonlinear kernel version. To reduce the computational complexity, we propose a practical parallel implementation of the method by introducing the Nyström approximation. We evaluate the parallel performance of our implementation using the Oakforest-PACS supercomputer.