重新思考LDA：离散ICA的矩匹配
Rethinking LDA: moment matching for discrete ICA
摘要：
我们考虑矩匹配技术估计潜在狄利克雷分配（LDA）。通过明确的LDA和离散版本的独立成分分析（ICA）之间的联系，我们首先推导出一组新的基于累积量的张量，与改进的样本复杂度。此外，我们重用标准的ICA技术，如联合对角化张量，以改善现有的方法的基础上张量功率方法。在合成和真实数据集上的一组广泛的实验中，我们证明了我们的张量和正交联合对角化技术的新组合优于现有的矩匹配方法。
英文摘要：
We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techniques such as joint diagonalization of tensors to improve over existing methods based on the tensor power method. In an extensive set of experiments on both synthetic and real datasets, we show that our new combination of tensors and orthogonal joint diagonalization techniques outperforms existing moment matching methods.
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