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Title: Multiway spherical clustering via degree-corrected tensor block models.
We consider the problem of multiway clus- tering in the presence of unknown degree heterogeneity. Such data problems arise commonly in applications such as recom- mendation system, neuroimaging, commu- nity detection, and hypergraph partitions in social networks. The allowance of de- gree heterogeneity provides great flexibility in clustering models, but the extra com- plexity poses significant challenges in both statistics and computation. Here, we de- velop a degree-corrected tensor block model with estimation accuracy guarantees. We present the phase transition of clustering performance based on the notion of an- gle separability, and we characterize three signal-to-noise regimes corresponding to dif- ferent statistical-computational behaviors. In particular, we demonstrate that an intrin- sic statistical-to-computational gap emerges only for tensors of order three or greater. Further, we develop an efficient polynomial- time algorithm that provably achieves exact clustering under mild signal conditions. The efficacy of our procedure is demonstrated through both simulations and analyses of Peru Legislation dataset.  more » « less
Award ID(s):
1915978
NSF-PAR ID:
10345851
Author(s) / Creator(s):
;
Date Published:
Journal Name:
The 25th International Conference on Artificial Intelligence and Statistics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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