Label propagation algorithm based on LDA model
Sun Jie-han · Journal of Computer Applications · 2012
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.