Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models

arXiv:2403.18517v1 Announce Type: new
Abstract: Regularized nonnegative low-rank approximations such as sparse Nonnegative Matrix Factorization or sparse Nonnegative Tucker Decomposition are an important branch of dimensionality reduction models with enhanced interpretability. However, from a practical perspective, the choice of regularizers and regularization coefficients, as well as the design of efficient algorithms, is challenging because of the multifactor nature of these models and the lack of theory to back these choices. This paper aims at improving upon these issues. By studying …

abstract algorithms approximation arxiv cs.lg cs.na design dimensionality factorization however interpretability low math.na math.oc matrix perspective practical regularization scale tucker type