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}
}
• 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

#### References

##### Publications referenced by this paper.
SHOWING 1-10 OF 35 REFERENCES