论文标题
确定点过程的启发式独立粒子近似
A heuristic independent particle approximation to determinantal point processes
论文作者
论文摘要
确定点过程是一个随机点过程，通常用于捕获负相关。近年来，它在机器学习中变得越来越流行。但是，对确定点过程进行采样仍然是一项计算密集的任务。本说明将启发式独立粒子近似引入确定点过程。近似值基于费米子的物理直觉，并使用标准数值线性代数例程实现。从这个独立的粒子近似中采样可以以微不足道的成本进行。提供数值结果以证明所提出的算法的性能。
A determinantal point process is a stochastic point process that is commonly used to capture negative correlations. It has become increasingly popular in machine learning in recent years. Sampling a determinantal point process however remains a computationally intensive task. This note introduces a heuristic independent particle approximation to determinantal point processes. The approximation is based on the physical intuition of fermions and is implemented using standard numerical linear algebra routines. Sampling from this independent particle approximation can be performed at a negligible cost. Numerical results are provided to demonstrate the performance of the proposed algorithm.
