Low rank approximation with sparse integration of multiple manifolds for data representation

@article{Tao2014LowRA,
  title={Low rank approximation with sparse integration of multiple manifolds for data representation},
  author={Liang Tao and Horace Ho-Shing Ip and Yinglin Wang and Xin Shu},
  journal={Applied Intelligence},
  year={2014},
  volume={42},
  pages={430-446}
}
Manifold regularized techniques have been extensively exploited in unsupervised learning like matrix factorization whose performance is heavily affected by the underlying graph regularization. However, there exist no principled ways to select reasonable graphs under the matrix decomposition setting, particularly in multiple heterogeneous graph sources. In this paper, we deal with the issue of searching for the optimal linear combination space of multiple graphs under the low rank matrix… CONTINUE READING