Open-set semi-supervised audio-visual speaker recognition using co-training LDA and Sparse Representation Classifiers

@article{Zhao2013OpensetSA,
  title={Open-set semi-supervised audio-visual speaker recognition using co-training LDA and Sparse Representation Classifiers},
  author={Xuran Zhao and Nicholas W. D. Evans and Jean-Luc Dugelay},
  journal={2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
  year={2013},
  pages={2999-3003}
}
Semi-supervised learning is attracting growing interest within the biometrics community. Almost all prior work focuses on closed-set scenarios, in which samples labelled automatically are assumed to belong to an enrolled class. This is often not the case in realistic applications and thus open-set alternatives are needed. This paper proposes a new approach to open-set, semi-supervised learning based on co-training, Linear Discriminant Analysis (LDA) subspaces and Sparse Representation… CONTINUE READING