SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

@article{Cai2008SRDAAE,
  title={SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis},
  author={Deng Cai and Xiaofei He and Jiawei Han},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2008},
  volume={20},
  pages={1-12}
}
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices… CONTINUE READING

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