Scalable Model-Based Clustering with Sequential Monte Carlo
For practitioners dealing with large-scale online clustering problems (e.g., knowledge base construction), this method offers a scalable alternative to traditional SMC, which suffers from prohibitive memory costs.
The paper proposes a novel Sequential Monte Carlo algorithm that decomposes clustering problems into approximately independent subproblems to reduce memory requirements, enabling accurate and efficient clustering in large-scale settings such as knowledge base construction.
In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data are observed. This difficulty is compounded when clusters follow complex distributions, as is the case with text data. Sequential Monte Carlo (SMC) methods give a natural way of representing and updating this uncertainty over time, but have prohibitive memory requirements for large-scale problems. We propose a novel SMC algorithm that decomposes clustering problems into approximately independent subproblems, allowing a more compact representation of the algorithm state. Our approach is motivated by the knowledge base construction problem, and we show that our method is able to accurately and efficiently solve clustering problems in this setting and others where traditional SMC struggles.