Semi-crowdsourced Clustering with Deep Generative Models
This addresses clustering with limited, noisy human input, which is incremental as it builds on existing crowdsourced clustering approaches.
The paper tackles the problem of semi-supervised clustering using noisy pairwise comparisons from crowdsourcing, proposing a deep generative model combined with a statistical relational model, and shows that it outperforms previous methods on synthetic and real-world datasets.
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.