The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group


with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

Miaoyan Wang,

Date and Time: Oct 17, 2018 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


Tensors of order 3 or greater, known as higher-order tensors, have recently attracted increased attention in many fields. Methods built on tensors provide powerful tools to capture complex structures in data that lower-order methods may fail to exploit. However, extending familiar matrix concepts to higher-order tensors is not straightforward, and indeed it has been shown that most computational problems for tensors are NP-hard. In this talk, I will present some statistical results on binary tensor decomposition. Instead of observing a real-valued higher-order tensor, we observe a binary tensor in which each tensor entry is quantized into a 0-1 measurement. We propose a constrained MLE and give the performance bound under a generalized multilinear model. The obtained rate is optimal in a minimax sense over a class of low-rank tensors. We demonstrate the power of our method on the tasks of tensor completion and clustering, with improved performance over state-of-the-art.