Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning

@article{Yang2013RobustNM,
  title={Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning},
  author={Shizhun Yang and C. Hou and C. Zhang and Yi Wu},
  journal={Neural Computing and Applications},
  year={2013},
  volume={23},
  pages={541-559}
}
  • Shizhun Yang, C. Hou, +1 author Yi Wu
  • Published 2013
  • Mathematics, Computer Science
  • Neural Computing and Applications
  • In real-world applications, we often have to deal with some high-dimensional, sparse, noisy, and non-independent identically distributed data. In this paper, we aim to handle this kind of complex data in a transfer learning framework, and propose a robust non-negative matrix factorization via joint sparse and graph regularization model for transfer learning. First, we employ robust non-negative matrix factorization via sparse regularization model (RSNMF) to handle source domain data and then… CONTINUE READING
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