论文标题
学习可靠的表示，以通过保存变异歧视网络的局部性聚类
Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network
论文作者
论文摘要
聚类是无监督学习的基本问题之一。最近的基于深度学习的方法着重于学习聚类的表示。在这些方法中，通过在潜在空间之前指定高斯混合物，变化深层嵌入在各种聚类任务中取得了巨大成功。但是，Vade遇到了两个问题：1）它对输入噪声脆弱； 2）它忽略了相邻数据点之间的局部信息。在本文中，我们提出了一个联合学习框架，该框架可以通过强大的嵌入歧视器和局部结构约束来改善VADE，这两者都有助于改善我们的模型的鲁棒性。各种视觉和文本数据集的实验结果表明，我们的方法在所有指标中都优于最先进的基线模型。进一步的详细分析表明，我们提出的模型对对抗性输入非常强大，这是实用应用的理想属性。
Clustering is one of the fundamental problems in unsupervised learning. Recent deep learning based methods focus on learning clustering oriented representations. Among those methods, Variational Deep Embedding achieves great success in various clustering tasks by specifying a Gaussian Mixture prior to the latent space. However, VaDE suffers from two problems: 1) it is fragile to the input noise; 2) it ignores the locality information between the neighboring data points. In this paper, we propose a joint learning framework that improves VaDE with a robust embedding discriminator and a local structure constraint, which are both helpful to improve the robustness of our model. Experiment results on various vision and textual datasets demonstrate that our method outperforms the state-of-the-art baseline models in all metrics. Further detailed analysis shows that our proposed model is very robust to the adversarial inputs, which is a desirable property for practical applications.
