We study a completion problem of broad practical interest: the reconstruction of a low-rank
symmetric tensor from highly incomplete and randomly corrupted observations of its entries …

Multiway data-related learning tasks pose a huge challenge to the traditional regression
analysis techniques due to the existence of multidirectional relatedness. Simply vectorizing …

Many applications produce multiway data of exceedingly high dimension. Modeling such
multi-way data is important in multichannel signal and video processing where sensors …

This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis
(STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with …

We consider the problem of jointly modeling and clustering populations of tensors by
introducing a high-dimensional tensor mixture model with heterogeneous covariances. To …

Multi-channel imaging data is a prevalent data format in scientific fields such as astronomy
and biology. The structured information and the high dimensionality of these 3-D tensor data …

Collecting neuroimaging data in the form of tensors (ie multidimensional arrays) has
become more common in mental health studies, driven by an increasing interest in studying …

We propose a novel use of a broadcasting operation, which distributes univariate functions
to all entries of the tensor covariate, to model the nonlinearity in tensor regression …

We consider parsimonious modeling of high-dimensional multivariate additive models using
regression splines, with or without sparsity assumptions. The approach is based on treating …

Due to the accelerated development and popularization of Internet, mobile Internet, and
Internet of Things and the breakthrough of storage and communication technologies, the …