  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.

Machine Learning;Machine Learning