Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image

@article{Kim2005LocallyLD,
  title={Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image},
  author={Tae-Kyun Kim and Josef Kittler},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2005},
  volume={27},
  pages={318-327}
}
We present a novel method of nonlinear discriminant analysis involving a set of locally linear transformations called "Locally Linear Discriminant Analysis" (LLDA). The underlying idea is that global nonlinear data structures are locally linear and local structures can be linearly aligned. Input vectors are projected into each local feature space by linear transformations found to yield locally linearly transformed classes that maximize the between-class covariance while minimizing the within… CONTINUE READING
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