Heteroskedastic Tensor Clustering

Understand

Tensor clustering, which seeks to extract underlying cluster structures from noisy tensor observations, has gained increasing attention.

• One extensively studied model for tensor clustering is the tensor block model, which postulates the existence of clustering structures along each mode and has found broad applications in areas like multi-tissue gene expression analysis and multilayer network analysis.
• However, currently available computationally feasible methods for tensor clustering either are limited to handling i.i.d.
• sub-Gaussian noise or suffer from suboptimal statistical performance, which restrains their utility in applications that have to deal with heteroskedastic data and/or low signal-to-noise-ratio (SNR).