A consistent and flexible framework for deep matrix factorizations

TL;DR

This paper introduces a consistent framework for deep matrix factorizations, proposing new loss functions and optimization methods that improve feature extraction in high-dimensional data, with applications in hyperspectral unmixing and facial feature analysis.

Contribution

It presents a novel, unified framework for deep matrix factorizations with consistent loss functions and flexible optimization, enhancing feature extraction capabilities.

Findings

Effective on synthetic and real datasets

Improves feature extraction with various constraints

Applicable to hyperspectral unmixing and facial features

Abstract

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 corresponding optimization problems. We illustrate the effectiveness of this approach through the integration of various constraints and regularizations, such as sparsity, nonnegativity and minimum-volume. The models are successfully applied on both synthetic and real data, namely for hyperspectral unmixing and extraction of facial features.