A number of recent works have studied algorithms for entrywise ℓp-low rank approximation,
namely algorithms which given an n× d matrix A (with n≥ d), output a rank-k matrix B …

We give faster approximation algorithms for well-studied variants of Binary Matrix
Factorization (BMF), where we are given a binary $ m\times n $ matrix $ A $ and would like …

We provide a randomized linear time approximation scheme for a generic problem about
clustering of binary vectors subject to additional constraints. The new constrained clustering …

Blind source separation (BSS) is a generic signal processing problem. BSS methods aim to
estimate a set of unknown source signals, by using a set of available signals that are …

Low-rank binary matrix approximation is a generic problem where one seeks a good
approximation of a binary matrix by another binary matrix with some specific properties. A …

Independent component analysis (ICA) is a statistical method for transforming an observable
multi-dimensional random vector into components that are as statistically independent as …

Low rank matrix approximation is an important tool in machine learning. Given a data matrix,
low rank approximation helps to find factors, patterns and provides concise representations …

Independent component analysis (ICA) is a statistical tool that decomposes an observed
random vector into components that are as statistically independent as possible. ICA over …

Abstract We study the $\ell_0 $-Low Rank Approximation Problem, where the goal is, given
an $ m\times n $ matrix $ A $, to output a rank-$ k $ matrix $ A'$ for which $\| A'-A\| _0 $ is …

The application of binary matrices are numerous. Representing a matrix as a mixture of a
small collection of latent vectors via low-rank decomposition is often seen as an …