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Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo · TIAN TIAN · Jiaxin Shi · Jun Zhu · Bo Zhang

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #59

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.

Author Information

Yucen Luo (Tsinghua University)
TIAN TIAN (Tsinghua University)
Jiaxin Shi (Tsinghua University)
Jun Zhu (Tsinghua University)
Bo Zhang (Tsinghua University)

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