We consider the problem of recovering a complete (ie, square and invertible) matrix A 0,
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …

One popular method for dealing with large-scale data sets is sampling. Using the empirical
statistical leverage scores as an importance sampling distribution, the method of algorithmic …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

Deep learning models have lately shown great performance in various fields such as
computer vision, speech recognition, speech translation, and natural language processing …

We analyze the matrix factorization problem. Given a noisy measurement of a product of two
matrices, the problem is to estimate back the original matrices. It arises in many applications …

A popular approach within the signal processing and machine learning communities
consists in modeling signals as sparse linear combinations of atoms selected from a learned …

We consider the problem of recovering a complete (ie, square and invertible) dictionary A 0,
from Y= A 0 X 0 with Y ϵ R n× p. This recovery setting is central to the theoretical …

We propose a novel formulation for dictionary learning that minimizes the determinant of the
dictionary matrix, also known as its volume, subject to the constraint that each row of the …

Abstract Knowledge transfer between tasks can improve the performance of learned models,
but requires an accurate estimate of inter-task relationships to identify the relevant …

Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based
interpretation and the effective multiplicative updating rule for searching local solutions. In …