论文标题
SOM-CPC：无监督的对比度学习，具有自组织图，用于高速时间序列的结构化表示
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
论文作者
论文摘要
在许多应用程序域中，使用数量不断增加的传感器的连续监测已变得无处不在。但是，获得的时间序列通常是高维且难以解释的。富有表现力的深度学习（DL）模型已降低维度，但产生的潜在空间通常仍然很难解释。在这项工作中，我们提出了SOM-CPC，该模型可以在有组织的2D歧管中可视化数据，同时保留较高维度的信息。我们讨论了一组构成高速时间序列的场景集，并在合成和现实生活数据（生理数据和音频记录）上显示，SOM-CPC的表现超过了基于DL的特征提取（例如基于DL的特征提取），然后是常规降低性降低性降低技术，以及与常规降低性降低技术以及模型的DL模型（SOM DL模型），并实现了SOM的模型。 SOM-CPC具有更好地了解高速数据流中潜在模式的巨大潜力。
Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. However, acquired time series are typically high-dimensional and difficult to interpret. Expressive deep learning (DL) models have gained popularity for dimensionality reduction, but the resulting latent space often remains difficult to interpret. In this work we propose SOM-CPC, a model that visualizes data in an organized 2D manifold, while preserving higher-dimensional information. We address a largely unexplored and challenging set of scenarios comprising high-rate time series, and show on both synthetic and real-life data (physiological data and audio recordings) that SOM-CPC outperforms strong baselines like DL-based feature extraction, followed by conventional dimensionality reduction techniques, and models that jointly optimize a DL model and a Self-Organizing Map (SOM). SOM-CPC has great potential to acquire a better understanding of latent patterns in high-rate data streams.
