Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition

@inproceedings{Zhu2017DoublyAM,
  title={Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition},
  author={Zeyuan Allen Zhu and Yuanzhi Li},
  booktitle={ICML},
  year={2017}
}
We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and on k. Furthermore, our algorithms are doubly-accelerated : our running times depend only on the square root of the matrix condition number, and on the square root of the eigengap. This is the first such… CONTINUE READING
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