Fractal Autoencoders for Feature Selection
Fractal Autoencoders for Feature Selection
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DOI：
10.1609/aaai.v35i12.17242
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Xinxing Wu;Q. Cheng
中科院分区：
文献类型：
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作者：
Xinxing Wu;Q. Cheng
特征选择通过识别最具信息量的特征子集来降低数据的维数。在本文中，我们提出了一种创新的无监督特征选择框架，称为分形自编码器（FAE）。它训练一个神经网络来精确定位信息特征，用于可表征性的全局探索和多样性的局部挖掘。在架构上，FAE通过添加一个一对一的评分层和一个小的子神经网络来扩展自编码器，以无监督的方式进行特征选择。凭借如此简洁的建筑，FAE实现了最先进的性能；在14个数据集上的大量实验结果，包括非常高维的数据，已经证明了FAE优于现有的无监督特征选择方法。特别是FAE在基因表达数据探索方面具有显著优势，比广泛使用的L1000标志性基因降低了约15%的测量成本。此外，我们还展示了FAE框架很容易通过应用程序进行扩展。
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.