LDA是一种文档主题生成模型，也称为一个三层贝叶斯概率模型，包含词、主题和文档三层结构。文档到主题服从Dirichlet分布，主题到词服从多项式分布。



LDA是一种非监督机器学习技术，可以用来识别大规模文档集（document collection）或语料库（corpus）中潜藏的主题信息。它采用了词袋（bag of words）的方法，这种方法将每一篇文档视为一个词频向量，从而将文本信息转化为了易于建模的数字信息。但是词袋方法没有考虑词与词之间的顺序，这简化了问题的复杂性，同时也为模型的改进提供了契机。每一篇文档代表了一些主题所构成的一个概率分布，而每一个主题又代表了很多单词所构成的一个概率分布。



对于语料库中的每篇文档，LDA定义了如下生成过程（generative process）：



1. 对每一篇文档，从主题分布中抽取一个主题；



2. 从上述被抽到的主题所对应的单词分布中抽取一个单词；



3. 重复上述过程直至遍历文档中的每一个单词。-LDA is a document theme generation model, also known as a three-tier Bayesian probability model that contains the words, topics and document three-tier structure. Dirichlet distribution of the document to the theme of obedience, the theme to the word obey polynomial distribution.



LDA is an unsupervised machine learning techniques can be used to identify large-scale document set (document collection) or corpus (corpus) of the underlying themes of information. It uses the word bag (bag of words) of the method, which each one document as a word frequency vector, thus the text information into digital information for ease of modeling. However, the method does not consider the order of the words Bag between words, which simplifies the complexity of the problem, but also for the improvement of the model provided an opportunity. Each document represents a probability distribution of some of the topics posed, and each topic and they represent many words constituted a probability distribut