ASKIT: An Efficient, Parallel Library for High-Dimensional Kernel Summations
ASKIT: An Efficient, Parallel Library for High-Dimensional Kernel Summations
复制标题
ASKIT：用于高维核求和的高效并行库
DOI：
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发表时间：
2016
影响因子：
3.1
通讯作者：
G. Biros
中科院分区：
文献类型：
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作者：
William B. March;Bo Xiao;Chenhan D. Yu;G. Biros
基于核的方法是各种机器学习和计算统计方法中的强大工具。这些方法的一个关键瓶颈是涉及核矩阵的计算，核矩阵随问题规模的二次扩展。在此之前，我们介绍了ASKIT (Approximate skeleton Kernel Independent Treecode)，这是一种高效、可扩展、核无关的方法，用于近似计算核矩阵向量积。ASKIT基于一种新颖的随机方法，利用近似最近邻信息有效地分解核矩阵的非对角线块。在这种情况下，ASKIT可以看作是任意维度的代数快速多极方法。在本文中，我们介绍了我们的开源实现ASKIT。我们的ASKIT库的特点包括对数据输入维数的线性依赖，在没有内核先验信息的情况下近似内核函数的能力，以及可扩展到成千上万的计算内核和数据。
Kernel-based methods are a powerful tool in a variety of machine learning and computational statistics methods. A key bottleneck in these methods is computations involving the kernel matrix, which scales quadratically with the problem size. Previously, we introduced ASKIT (Approximate Skeletonization Kernel Independent Treecode), an efficient, scalable, kernel-independent method for approximately evaluating kernel matrix-vector products. ASKIT is based on a novel, randomized method for efficiently factoring off-diagonal blocks of the kernel matrix using approximate nearest neighbor information. In this context, ASKIT can be viewed as an algebraic fast multipole method for arbitrary dimensions. In this paper, we introduce our open-source implementation of ASKIT. Features of our ASKIT library include linear dependence on the input dimension of the data, the ability to approximate kernel functions with no prior information on the kernel, and scalability to tens of thousands of compute cores and data with bil...