A consistent and flexible framework for deep matrix factorizations. (arXiv:2206.10693v1 [cs.LG])

Web: http://arxiv.org/abs/2206.10693

Deep matrix factorizations (deep MFs) are recent unsupervised data mining
techniques inspired by constrained low-rank approximations. They aim to extract
complex hierarchies of features within high-dimensional datasets. Most of the
loss functions proposed in the literature to evaluate the quality of deep MF
models and the underlying optimization frameworks are not consistent because
different losses are used at different layers. In this paper, we introduce two
meaningful loss functions for deep MF and present a generic framework to solve
the …

arxiv consistent deep framework lg