The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as
the product of two low-rank binary factor matrices, where the product of the factor matrices is …

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 …

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 …

Given a collection of n points in ℝ d, the goal of the (k, z)-clustering problem is to find a
subset of k “centers” that minimizes the sum of the z-th powers of the Euclidean distance of …

In this work, we initiate the study of\emph {Dynamic Tensor Product Regression}. One has
matrices $ A_1\in\mathbb {R}^{n_1\times d_1},\ldots, A_q\in\mathbb {R}^{n_q\times d_q} …

Subset selection for the rank k approximation of an n× d matrix A offers improvements in the
interpretability of matrices, as well as a variety of computational savings. This problem is well …

We study the Kronecker product regression problem, in which the design matrix is a
Kronecker product of two or more matrices. Formally, given $ A_i\in\R^{n_i\times d_i} $ for …

In this paper, we develop a novel procedure for low-rank tensor regression, namely
Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind …

We study iterative methods based on Krylov subspaces for low-rank approximation under
any Schatten-p norm. Here, given access to a matrix A through matrix-vector products, an …

We create classical (non-quantum) dynamic data structures supporting queries for
recommender systems and least-squares regression that are comparable to their quantum …