Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end  study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based ighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data.
['Mikhail Yurochkin', 'XuanLong Nguyen']
I am writing a research paper on a idea Geometric Dirichlet Means algorithm for topic inference
[{"from": "human", "value": "I am writing a research paper on a idea Geometric Dirichlet Means algorithm for topic inference"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end  study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based ighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data."}]