Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification

  title={Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification},
  author={Sampath Deegalla and Henrik Bostr{\"o}m},
  journal={2006 5th International Conference on Machine Learning and Applications (ICMLA'06)},
The computational cost of using nearest neighbor classification often prevents the method from being applied in practice when dealing with high-dimensional data, such as images and micro arrays. One possible solution to this problem is to reduce the dimensionality of the data, ideally without loosing predictive performance. Two different dimensionality reduction methods, principle component analysis (PCA) and random projection (RP), are investigated for this purpose and compared w.r.t. the… CONTINUE READING
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