Abstract
Non-negative Matrix Factorization (NMF) and
spectral clustering have been proved to be efficient
and effective for data clustering tasks and have
been applied to various real-world scenes. However, there are still some drawbacks in traditional
methods: (1) most existing algorithms only consider high-dimensional data directly while neglect
the intrinsic data structure in the low-dimensional
subspace; (2) the pseudo-information got in the
optimization process is not relevant to most spectral clustering and manifold regularization methods. In this paper, a novel unsupervised matrix factorization method, Pseudo Supervised Matrix Factorization (PSMF), is proposed for data clustering.
The main contributions are threefold: (1) to cluster
in the discriminant subspace, Linear Discriminant
Analysis (LDA) combines with NMF to become a
unified framework; (2) we propose a pseudo supervised manifold regularization term which utilizes
the pseudo-information to instruct the regularization term in order to find subspace that discriminates different classes; (3) an efficient optimization algorithm is designed to solve the proposed
problem with proved convergence. Extensive experiments on multiple benchmark datasets illustrate
that the proposed model outperforms other state-ofthe-art clustering algorithms