基于最大相关熵非负矩阵分解的文本聚类
Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization
摘要：
非负矩阵分解（NMF）已成功地应用于许多领域的分类和聚类。常用的NMF算法主要以最小化$l_2$距离或KL散度为目标，不适合非线性情况。本文提出了一种新的分解方法，通过最大化原始矩阵和两个低秩矩阵的乘积之间的相关熵来进行文档聚类。该方法还允许我们从数据中学习语义特征空间的新基向量。据我们所知，我们还没有看到任何工作已经做了最大化相关熵的NMF聚类高维文档数据。我们的实验结果表明，我们提出的方法优于其他变种的NMF算法在路透社21578和TDT2数据库。
英文摘要：
Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
