论文标题
用于深集聚类的异差混合物自动编码器
Dissimilarity Mixture Autoencoder for Deep Clustering
论文作者
论文摘要
差异混合物自动编码器（DMAE）是一种基于特征聚类的神经网络模型，它包含了灵活的差异功能，并且可以集成到任何类型的深度学习体系结构中。它在内部代表了一种差异混合模型（DMM），该模型扩展了诸如K-均值，高斯混合模型或Bregman聚类（通过将概率作为神经网络表示）重新解释为任何凸的且可区分的差异函数的经典方法。 DMAE可以将深度学习体系结构集成到端到端模型中，从而可以同时估计聚类和神经网络的参数。对图像和文本聚类基准数据集进行了实验评估，以表明DMAE在无监督分类精度和归一化互信息方面具有竞争力。实施DMAE的源代码可在以下网址公开获取：https：//github.com/juselara1/dmae
The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. It internally represents a dissimilarity mixture model (DMM) that extends classical methods like K-Means, Gaussian mixture models, or Bregman clustering to any convex and differentiable dissimilarity function through the reinterpretation of probabilities as neural network representations. DMAE can be integrated with deep learning architectures into end-to-end models, allowing the simultaneous estimation of the clustering and neural network's parameters. Experimental evaluation was performed on image and text clustering benchmark datasets showing that DMAE is competitive in terms of unsupervised classification accuracy and normalized mutual information. The source code with the implementation of DMAE is publicly available at: https://github.com/juselara1/dmae
