Thresholding Based Outlier Robust PCA

Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
Proceedings of the 2017 Conference on Learning Theory, PMLR 65:593-628, 2017.

Abstract

We consider the problem of outlier robust PCA (\textbfOR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-α)$ fraction of the points are noisy samples from a low-dimensional subspace while $α$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \textbfOR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $α$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers.

Cite this Paper


BibTeX
@InProceedings{pmlr-v65-cherapanamjeri17a, title = {Thresholding Based Outlier Robust PCA}, author = {Cherapanamjeri, Yeshwanth and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Proceedings of the 2017 Conference on Learning Theory}, pages = {593--628}, year = {2017}, editor = {Kale, Satyen and Shamir, Ohad}, volume = {65}, series = {Proceedings of Machine Learning Research}, month = {07--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v65/cherapanamjeri17a/cherapanamjeri17a.pdf}, url = { http://proceedings.mlr.press/v65/cherapanamjeri17a.html }, abstract = {We consider the problem of outlier robust PCA (\textbfOR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-α)$ fraction of the points are noisy samples from a low-dimensional subspace while $α$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \textbfOR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $α$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers.} }
Endnote
%0 Conference Paper %T Thresholding Based Outlier Robust PCA %A Yeshwanth Cherapanamjeri %A Prateek Jain %A Praneeth Netrapalli %B Proceedings of the 2017 Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2017 %E Satyen Kale %E Ohad Shamir %F pmlr-v65-cherapanamjeri17a %I PMLR %P 593--628 %U http://proceedings.mlr.press/v65/cherapanamjeri17a.html %V 65 %X We consider the problem of outlier robust PCA (\textbfOR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-α)$ fraction of the points are noisy samples from a low-dimensional subspace while $α$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \textbfOR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $α$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers.
APA
Cherapanamjeri, Y., Jain, P. & Netrapalli, P.. (2017). Thresholding Based Outlier Robust PCA. Proceedings of the 2017 Conference on Learning Theory, in Proceedings of Machine Learning Research 65:593-628 Available from http://proceedings.mlr.press/v65/cherapanamjeri17a.html .

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