Analysis on Non-negative Factorizations and Applications

TL;DR

This paper analyzes non-negative matrix factorizations (NMF), introduces a sparse low-rank tensor approximation, and proposes a multi-level analysis framework and a semi-smooth Newton method, demonstrating their effectiveness in imaging and inverse problems.

Contribution

It introduces a novel multi-level analysis framework and a semi-smooth Newton method for NMF, enhancing feature extraction and sparse representation in imaging applications.

Findings

Effective feature capture in images and inverse problems

Sparse low-rank representations of data achieved

Proposed methods outperform traditional approaches

Abstract

In this work we perform some mathematical analysis on non-negative matrix factorizations (NMF) and apply NMF to some imaging and inverse problems. We will propose a sparse low-rank approximation of big positive data and images in terms of tensor products of positive vectors, and investigate its effectiveness in terms of the number of tensor products to be used in the approximation. A new concept of multi-level analysis (MLA) framework is also suggested to extract major components in the matrix representing structures of different resolutions, but still preserving the positivity of the basis and sparsity of the approximation. We will also propose a semi-smooth Newton method based on primal-dual active sets for the non-negative factorization. Numerical results are given to demonstrate the effectiveness of the proposed method to capture features in images and structures of inverse problems under no a-priori assumption on the data structure, as well as to provide a sparse low-rank representation of the data.