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DESCRIPTION:Speaker: Grigory Yaroslavtsev, Assistant Professor of Statistics at Indiana University
Talk Title: Advances in Hierarchical Clustering of Vector Data
Abstract: Compared to the highly successful flat clustering (e.g. k-means), despite its important role and applications in data analysis, hierarchical clustering has been lacking in rigorous algorithmic studies until late due to absence of rigorous objectives. Since 2016, a sequence of works has emerged and gave novel algorithms for this problem in the general metric setting. This was enabled by a breakthrough by Dasgupta, who introduced a formal objective into the study of hierarchical clustering.\n
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In this talk I will give an overview of our recent progress on models and scalable algorithms for hierarchical clustering applicable specifically to high-dimensional vector data, including embedding vectors arising from deep learning. I will first discuss various linkage-based algorithms (single-linkage, average-linkage) and their formal properties with respect to various objectives. I will then introduce a new projection-based approximation algorithm for vector data. The talk will be self-contained and does not assume prior knowledge of clustering methods.
Host: Shaddin Dughmi
SEQUENCE:5
DTSTART:20191010T121500
LOCATION:SAL 213
DTSTAMP:20191010T121500
SUMMARY:Theory Lunch
UID:EC9439B1-FF65-11D6-9973-003065F99D04
DTEND:20191010T140000
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