title: Low-Rank Matrix Factorization With  Adaptive Graph Regularizer.
abstract: In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.
authors.forename: Gui-Fu
authors.initials: GF
authors.lastname: Lu
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Jian
authors.initials: J
authors.lastname: Zou