Corpus ID: 6869227

Streaming Kernel Principal Component Analysis

@article{Ghashami2016StreamingKP,
  title={Streaming Kernel Principal Component Analysis},
  author={M. Ghashami and D. Perry and J. M. Phillips},
  journal={ArXiv},
  year={2016},
  volume={abs/1512.05059}
}
  • M. Ghashami, D. Perry, J. M. Phillips
  • Published 2016
  • Mathematics, Computer Science
  • ArXiv
  • Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nystr\"om method and random feature maps can help towards this goal, but they do not explicitly… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES
    An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection
    • 20
    • Highly Influential
    Subspace Embeddings for the Polynomial Kernel
    • 54
    • PDF
    A fast incremental Kernel Principal Component Analysis for learning stream of data chunks
    • 7
    • Highly Influential
    On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
    • 767
    • Highly Influential
    • PDF
    Compact Random Feature Maps
    • 58
    • PDF
    Improved Approximation Algorithms for Large Matrices via Random Projections
    • 587
    Randomized Nonlinear Component Analysis
    • 123
    • Highly Influential
    • PDF
    Random Fourier Approximations for Skewed Multiplicative Histogram Kernels
    • 68
    • PDF
    Random Features for Large-Scale Kernel Machines
    • 2,060
    • Highly Influential
    • PDF