Misc,
Online Matrix Factorization via Broyden Updates
(2015)cite arxiv:1506.04389Comment: Submitted.
Abstract
In this paper, we propose an online algorithm to compute matrix
factorizations. Proposed algorithm updates the dictionary matrix and associated
coefficients using a single observation at each time. The algorithm performs
low-rank updates to dictionary matrix. We derive the algorithm by defining a
simple objective function to minimize whenever an observation is arrived. We
extend the algorithm further for handling missing data. We also provide a
mini-batch extension which enables to compute the matrix factorization on big
datasets. We demonstrate the efficiency of our algorithm on a real dataset and
give comparisons with well-known algorithms such as stochastic gradient matrix
factorization and nonnegative matrix factorization (NMF).
