论文标题
用于功能选择的分形自动编码器
Fractal Autoencoders for Feature Selection
论文作者
论文摘要
特征选择通过识别最有用的特征的子集来降低数据的维度。在本文中，我们提出了一个无监督特征选择的创新框架，称为分形自动编码器（FAE）。它训练一个神经网络，以查明信息丰富的特征，以探索代表性和本地发掘多样性的功能。在建筑上，FAE通过添加一对一的评分层和一个小型的次神经网络来扩展自动编码器，以无监督的方式进行特征选择。 Fae具有如此简洁的建筑，实现了最先进的表演；在包括非常高维数据在内的十四个数据集上的广泛实验结果已经证明了FAE的优越性，而不是现代方法，用于无监督的特征选择。特别是，FAE在基因表达数据探索方面具有很大的优势，比广泛使用的L1000地标基因降低了$ 15 $ \％。此外，我们证明了FAE框架很容易通过应用程序扩展。
Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of representability and for local excavating of diversity. Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer and a small sub-neural network for feature selection in an unsupervised fashion. With such a concise architecture, FAE achieves state-of-the-art performances; extensive experimental results on fourteen datasets, including very high-dimensional data, have demonstrated the superiority of FAE over existing contemporary methods for unsupervised feature selection. In particular, FAE exhibits substantial advantages on gene expression data exploration, reducing measurement cost by about $15$\% over the widely used L1000 landmark genes. Further, we show that the FAE framework is easily extensible with an application.
