无监督机器学习拓扑特征
Featuring the topology with the unsupervised machine learning
摘要：
线描图像一般由基本元素组成。表征图像的最基本的元素之一是拓扑;线段属于不同于闭合圆的类别，并且具有不同缠绕度的闭合圆是不等价的。我们使用无监督的机器学习来研究具有非平凡缠绕的图像。我们构建了一个自动编码器模型，结合了卷积和全连接神经网络。我们确认，从训练模型过滤的压缩数据保留超过90%的正确信息的拓扑结构，证明图像聚类的无监督学习功能的拓扑结构。
英文摘要：
Images of line drawings are generally composed of primitive elements. One of the most fundamental elements to characterize images is the topology; line segments belong to a category different from closed circles, and closed circles with different winding degrees are nonequivalent. We investigate images with nontrivial winding using the unsupervised machine learning. We build an autoencoder model with a combination of convolutional and fully connected neural networks. We confirm that compressed data filtered from the trained model retain more than 90% of correct information on the topology, evidencing that image clustering from the unsupervised learning features the topology.
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