Deep auto-encoder based clustering
Deep auto-encoder based clustering
复制标题
基于深度自动编码器的聚类
DOI：
10.3233/ida-140709
复制
发表时间：
2014-01-01
影响因子：
1.7
通讯作者：
Wang, Liang
中科院分区：
文献类型：
--
作者：
Song, Chunfeng;Huang, Yongzhen;Wang, Liang
对于无监督的问题，如聚类，线性或非线性数据变换是广泛使用的技术。一般来说，它们有利于数据表示。然而，如果数据具有复杂的结构，这些技术将不能令人满意的聚类。在本文中，我们提出了一种新的聚类方法的基础上，深度自动编码器网络，它可以学习一个高度非线性映射函数。通过同时考虑数据重构和紧致性，该方法可以获得稳定有效的聚类结果。在四个数据库上的实验结果表明，该模型在归一化互信息、聚类纯度和准确性方面都有较好的性能。
For unsupervised problems like clustering, linear or non-linear data transformations are widely used techniques. Generally, they are beneficial to data representation. However, if data have a complicated structure, these techniques would be unsatisfying for clustering. In this paper, we propose a new clustering method based on the deep auto-encoder network, which can learn a highly non-linear mapping function. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experimental results on four databases demonstrate that the proposed model can achieve promising performance in terms of normalized mutual information, cluster purity and accuracy.