Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection

@article{Jiang2016DimensionalityRO,
  title={Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection},
  author={Rui Jiang and Weijie Fu and Li Wen and Shijie Hao and Richang Hong},
  journal={Neurocomputing},
  year={2016},
  volume={187},
  pages={109-118}
}
Manifold learning based dimensionality reduction methods have been successfully applied in many pattern recognition tasks, due to their ability to well capture the underlying relationship between data points. These methods, however, meet some challenges in terms of the storage cost and the computation complexity with the rapidly increasing data size. We propose an improved dimensionality reduction algorithm called Anchorgraph-based Locality Preserving Projection (AgLPP), trying to cope with the… CONTINUE READING

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