Open Science Research Excellence
@article{(International Science Index):,
  title    = {Non-negative Principal Component Analysis for Face Recognition},
  author    = {Zhang Yan and  Yu Bin},
  country   = {},
  abstract  = {Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {4},
  number    = {12},
  year      = {2010},
  pages     = {1794 - 1798},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 48, 2010},