二次神经元赋能的异构自编码器用于无监督异常检测
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
AI总结：
本文提出由传统神经元与二次神经元构成的异构自编码器，利用其高特征表示能力处理无监督异常检测中的数据未知性、异常异构性和特征不易察觉性，并在表格数据和轴承故障信号上取得有竞争力的效果。
摘要：
受生物神经元复杂性与多样性的启发，提出了一种二次神经元，用简化的二次函数替代当前神经元中的内积。采用这种新型神经元为发展深度学习提供了新的视角。在分析二次神经元时，我们发现存在一个函数，使得异构网络能够以多项式数量的神经元很好地逼近该函数，而纯传统网络或纯二次网络则需要指数数量的神经元才能达到相同的误差水平。受这一关于异构网络的启发性理论结果的鼓舞，我们直接在自编码器中集成传统神经元和二次神经元，构建了一种新型异构自编码器。据我们所知，这是第一个由不同类型神经元构成的异构自编码器。接下来，我们将所提出的异构自编码器应用于表格数据和轴承故障信号的无监督异常检测。异常检测面临数据未知性、异常特征异构性和特征不易察觉性等困难，这适合所提出的异构自编码器。其高特征表示能力能够刻画多种多样的异常数据（异构性），将异常与正常区分开来（不易察觉性），并准确学习正常样本的分布（未知性）。实验表明，与其他最先进模型相比，异构自编码器表现出具有竞争力的性能。
英文摘要：
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function. Employing such a novel type of neurons offers a new perspective on developing deep learning. When analyzing quadratic neurons, we find that there exists a function such that a heterogeneous network can approximate it well with a polynomial number of neurons but a purely conventional or quadratic network needs an exponential number of neurons to achieve the same level of error. Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders. To our best knowledge, it is the first heterogeneous autoencoder that is made of different types of neurons. Next, we apply the proposed heterogeneous autoencoder to unsupervised anomaly detection for tabular data and bearing fault signals. The anomaly detection faces difficulties such as data unknownness, anomaly feature heterogeneity, and feature unnoticeability, which is suitable for the proposed heterogeneous autoencoder. Its high feature representation ability can characterize a variety of anomaly data (heterogeneity), discriminate the anomaly from the normal (unnoticeability), and accurately learn the distribution of normal samples (unknownness). Experiments show that heterogeneous autoencoders perform competitively compared to other state-of-the-art models.
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。
