Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference
稀疏主题模型：计算效率、近最优算法与统计推断
Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is…