lda是一个集合概率模型,主要用于处理离散的数据集合,目前主要用在数据挖掘（dm）中的text mining和自然语言处理中,主要是用来降低维度的.据说效果不错.
以下是在tm中对lda的定义：
Latent Dirichlet allocation (LDA) is a generative probabilistic model of a corpus.The basic idea is
that documents are represented as random mixtures over latent topics,where each topic is characterized
by a distribution over words.
We describe latent Dirichlet allocation (LDA),a generative probabilistic model for collections of
discrete data such as text corpora.LDA is a three-level hierarchical Bayesian model,in which each
item of a collection is modeled as a finite mixture over an underlying set of topics.Each topic is,in
turn,modeled as an infinite mixture over an underlying set of topic probabilities.In the context of
text modeling,the topic probabilities provide an explicit representation of a document.
其实它还可以用在别的方面,早期是被用在自然语言处理的文本表示方面.因为他提供了一个理解相关词为什么在同一文档出现的框架解释模型.