Locally Weighted Prediction Methods for Latent Factor Analysis With Supervised and Semisupervised Process Data

@article{Yao2017LocallyWP,
  title={Locally Weighted Prediction Methods for Latent Factor Analysis With Supervised and Semisupervised Process Data},
  author={Le Yao and Zhiqiang Ge},
  journal={IEEE Transactions on Automation Science and Engineering},
  year={2017},
  volume={14},
  pages={126-138}
}
Through calculating the similarity between the historical and the new query data samples, a probabilistic locally weighted prediction method based on supervised latent factor analysis (SLFA) model is proposed. In this method, the contributions of different historical samples are expressed through incorporating the similarity index into the noise variance of… CONTINUE READING