Abstract
If data in ℝd actually lie in a linear subspace, then principal component analysis (PCA) will find this subspace. If the data are corrupted by benign (eg. independent Gaussian) noise, then approximation bounds can quite easily be shown for the solution returned by PCA. What if the noise is malicious?
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Dunagan, J., Vempala, S.: Optimal outlier removal in high-dimensional spaces. In: Proceedings of the 32nd ACM Symposium on the Theory of Computing (2000)
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© 2003 Springer-Verlag Berlin Heidelberg
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Dasgupta, S. (2003). Subspace Detection: A Robust Statistics Formulation. In: Schölkopf, B., Warmuth, M.K. (eds) Learning Theory and Kernel Machines. Lecture Notes in Computer Science(), vol 2777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45167-9_55
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DOI: https://doi.org/10.1007/978-3-540-45167-9_55
Publisher Name: Springer, Berlin, Heidelberg
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