Clustering is widely used in unsupervised learning method that deals with unlabeled data. Deep clustering has become a popular study area that relates clustering with Deep Neural Network (DNN) architecture. Deep clustering method downsamples high dimensional data, which may also relate clustering loss. Deep clustering is also introduced in semi-supervised learning (SSL). Most SSL methods depend on pairwise constraint information, which is a matrix containing knowledge if data pairs can be in the same cluster or not. This paper introduces a novel embedding system named AutoEmbedder, that downsamples higher dimensional data to clusterable embedding points. To the best of our knowledge, this is the first research endeavor that relates to traditional classifier DNN architecture with a pairwise loss reduction technique. The training process is semi-supervised and uses Siamese network architecture to compute pairwise constraint loss in the feature learning phase. The AutoEmbedder outperforms most of the existing DNN based semi-supervised methods tested on famous datasets.

聚类广泛用于处理未标记数据的无监督学习方法。深度集群已成为将集群与深度神经网络（DNN）架构相关联的热门研究领域。深度聚类方法对高维数据进行下采样，这也可能与聚类损失有关。在半监督学习（SSL）中也引入了深度集群。大多数SSL方法依赖于成对约束信息，该信息是一个矩阵，其中包含数据对是否可以在同一群集中的知识。本文介绍了一种名为AutoEmbedder的新型嵌入系统，该系统将较高维度的数据下采样到可聚类的嵌入点。据我们所知，这是首次采用成对损耗降低技术与传统分类器DNN架构相关的研究工作。训练过程是半监督的，并使用暹罗网络体系结构来计算特征学习阶段的成对约束损失。AutoEmbedder优于在著名数据集上测试过的大多数现有的基于DNN的半监督方法。