Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality
reduction, has been in the ascendant since its inception. It incorporates the nonnegativity …

This paper considers regularized block multiconvex optimization, where the feasible set and
objective function are generally nonconvex but convex in each block of variables. It also …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

We propose to detect abnormal events via a sparse reconstruction over the normal bases.
Given a collection of normal training examples, eg, an image sequence or a collection of …

Tensors have found application in a variety of fields, ranging from chemometrics to signal
processing and beyond. In this paper, we consider the problem of multilinear modeling of …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

The rapidly increasing availability of electronic health records (EHRs) from multiple
heterogeneous sources has spearheaded the adoption of data-driven approaches for …

Spatial gene expression patterns enable the detection of local covariability and are
extremely useful for identifying local gene interactions during normal development. The …

We propose a novel sparse tensor decomposition method, namely the tensor truncated
power method, that incorporates variable selection in the estimation of decomposition …

Motivated by applications in neuroimaging analysis, we propose a new regression model,
Sparse TensOr REsponse regression (STORE), with a tensor response and a vector …