PairNets：一种新的基于分块子空间的快速浅层人工神经网络
PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces
摘要：
传统上，人工神经网络（ANN）通过诸如反向传播算法的梯度下降算法来缓慢地训练，因为ANN的大量超参数需要用许多训练时期来微调。为了加快训练速度，我们创建了一种新型的浅层4层ANN，称为“成对神经网络“（“PairNet”），具有高速超参数优化。另外，每个输入的值被划分为多个区间，然后n维空间被划分为M个n维子空间。在M个局部n维子空间中构造M个局部PairNet。局部PairNet只需一个epoch即可快速训练，因为它的超参数通过使用多元最小二乘拟合方法简单地求解线性方程组直接优化一次。三个回归问题的仿真结果表明，对网络实现了更高的速度和更低的平均测试均方误差（MSE）的三种情况下，和更低的平均训练MSE的两种情况下，比传统的人工神经网络。基于智能方法和并行计算方法的优化算法是一个重要的未来工作，以优化分区的子空间和超参数，建立快速和有效的PairNet应用在大数据采掘和实时机器学习。
英文摘要：
Traditionally, an artificial neural network (ANN) is trained slowly by a gradient descent algorithm such as the backpropagation algorithm since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs. To highly speed up training, we created a novel shallow 4-layer ANN called "Pairwise Neural Network" ("PairNet") with high-speed hyperparameter optimization. In addition, a value of each input is partitioned into multiple intervals, and then an n-dimensional space is partitioned into M n-dimensional subspaces. M local PairNets are built in M partitioned local n-dimensional subspaces. A local PairNet is trained very quickly with only one epoch since its hyperparameters are directly optimized one-time via simply solving a system of linear equations by using the multivariate least squares fitting method. Simulation results for three regression problems indicated that the PairNet achieved much higher speeds and lower average testing mean squared errors (MSEs) for the three cases, and lower average training MSEs for two cases than the traditional ANNs. A significant future work is to develop better and faster optimization algorithms based on intelligent methods and parallel computing methods to optimize both partitioned subspaces and hyperparameters to build the fast and effective PairNets for applications in big data mining and real-time machine learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
