Open AccessAn efficient ADMM-type algorithm for deep semi-nonnegative matrix factorization
Author(s) -
Yijia Zhou,
Lijun Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1592/1/012043
Subject(s) - non negative matrix factorization , karush–kuhn–tucker conditions , focus (optics) , algorithm , computer science , cluster analysis , matrix (chemical analysis) , matrix decomposition , artificial intelligence , mathematical optimization , mathematics , eigenvalues and eigenvectors , physics , materials science , composite material , quantum mechanics , optics
In this paper, we focus on deep semi-nonnegative matrix factorization (DSemiNMF) which has a wider application in the real world than traditional NMF. We propose an efficient algorithm based on the classic alternating direction method of multipliers (ADMM) for DSemiNMF. By utilizing structures in DSemiNMF, we derive an efficient updating rule for updating subproblems according to its KKT conditions. Numerical experiments are conducted to compare the proposed algorithm with state-of-the-art deep semi-NMF algorithm. Results show that our algorithm performs better and the deep model indeed results in better clustering accuracy than single-layer model.