Simultaneous Hierarchical Topic Selection and Initialization for Latent Nested Dirichlet Allocation

Despite the widespread utilization of LDA in the context of topic modeling, there is a lack of research attention to the importance of such limitations.

To this end, we introduce a hierarchical topic model based on the Nested Dirichlet distribution, called Latent Nested Dirichlet Allocation (LNDA). Our novel framework includes the derivation of the complete variational EM inference for LNDA with a novel Hessian factorization, and integrating pruning/splitting initialization with RPC-based topic selection within the hierarchical NDD structure. Therefore, it initializes the model while simultaneously selecting the optimal number of topics based on the perplexity and rate of perplexity change, respectively.