Dirichlet Simplex Nest and Geometric Inference
Dirichlet Simplex Nest and Geometric Inference
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发表时间：
2019-05
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通讯作者：
M. Yurochkin;Aritra Guha;Yuekai Sun;X. Nguyen
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作者：
M. Yurochkin;Aritra Guha;Yuekai Sun;X. Nguyen
我们提出了狄利克雷单纯形巢，一类概率模型适合于各种数据类型，并开发快速和可证明准确的推理算法占模型的凸几何和低维单纯形结构。通过利用连接到Voronoi曲面细分和Dirichlet分布的属性，所提出的推理算法实现了一致性和强误差界保证的范围内的模型设置和数据分布。我们的模型和学习算法的有效性证明了模拟和文本和金融数据的分析。
We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional simplicial structure. By exploiting the connection to Voronoi tessellation and properties of Dirichlet distribution, the proposed inference algorithm is shown to achieve consistency and strong error bound guarantees on a range of model settings and data distributions. The effectiveness of our model and the learning algorithm is demonstrated by simulations and by analyses of text and financial data.