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 …

In this paper, a novel approach to fault detection for nonlinear processes is proposed. It is
based on a manifold learning called modified kernel semi-supervised local linear …

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 …

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 …

We transfer the ICA model to the case where the underlying field is not the set of reals but an
arbitrary finite field. We give conditions for separability of the model, pointing out existing …

In this paper, a new large-scale process monitoring method based on knowledge mining is
proposed. The contributions are as follows: 1) between-mode independent similarities are …

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 …