论文标题
尺寸估计的加法自动编码器
An Additive Autoencoder for Dimension Estimation
论文作者
论文摘要
提出和分析了一个由串行执行的偏差估计，线性趋势估计和非线性残留估计组成的尺寸缩小的加性自动编码器。计算实验证实，该表单的自动编码器仅具有浅网络以封装非线性行为，能够识别具有低自动编码误差的数据集的固有维度。该观察结果导致了一项调查，其中比较了浅层和深层网络结构及其训练方式。我们得出的结论是，在识别固有维度的情况下，更深的网络结构在识别内部的自动编码误差中会产生较低的自动编码误差。但是，与浅网络相比，检测到的维度不会改变。
An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsic dimension of a dataset with a low autoencoding error. This observation leads to an investigation in which shallow and deep network structures, and how they are trained, are compared. We conclude that the deeper network structures obtain lower autoencoding errors during the identification of the intrinsic dimension. However, the detected dimension does not change compared to a shallow network.
