摘要
标签传递算法是一种半监督分类方法,由于该算法存在要求数据分类结果符合流行假设、数据维数较高时计算复杂度高等问题,在文本分类中效果较差。针对这些问题,经过对LDA主题模型和标签传递算法原理及复杂度的分析,将两者结合,提出一种基于LDA主题模型的标签传递算法LPLDA。该算法用LDA主题模型中的主题表示文本数据,一方面使用LDA主题模型表示文本保证分类结果符合流行假设,另一方面有效减少标签传递算法相似度计算时间。经过实验证明,该算法在标记数据少于待测样本时,分类效果优于传统的有监督分类方法。
Label Propagation （LP） algorithm is one kind of semi-supervised learning methods. However, its performance in text classification is not good enough, because LP algorithm demands manifold assumption and it has high computational complexity in calculating the similarity of high dimension data. A new method was proposed to combine Latent Dirichlet Allocation （LDA） model with LP algorithm to solve the above problems after analyzing their principles and complexities. It represented documents with latent topics in LDA. On one hand, it reduces the dimension of matrixes; on the other hand, it can help LDA model lead to the classification results with manifold assumption. The experimental results show that the new method performs better than traditional supervised text classification methods in testing sets when labeled data is less than unlabeled data.
出处
《计算机应用》
CSCD
北大核心
2012年第2期403-406,410,共5页
journal of Computer Applications
关键词
LDA主题模型
标签传递算法
半监督学习
数据降维
流行假设
Latent Diriehlet Allocation （LDA） model
Label Propagation （LP） algorithm
semi-supervised learning
dimensional reduction
manifold assumption
