Invariance of principal components under low-dimensional random projection of the data

@article{Qi2012InvarianceOP,
  title={Invariance of principal components under low-dimensional random projection of the data},
  author={Hanchao Qi and Shannon M. Hughes},
  journal={2012 19th IEEE International Conference on Image Processing},
  year={2012},
  pages={937-940}
}
Algorithms that can efficiently recover principal components of high-dimensional data from compressive sensing measurements (e.g. low-dimensional random projections) of it have been an important topic of recent interest in the literature. In this paper, we show that, under certain conditions, normal principal component analysis (PCA) on such low-dimensional random projections of data actually returns the same result as PCA on the original data set would. In particular, as the number of data… CONTINUE READING
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