Parallel Active Subspace Decomposition for Tensor Robust Principal Component Analysis
Author:
Funder
HKRGC
Publisher
Springer Science and Business Media LLC
Subject
General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s42967-020-00063-9.pdf
Reference29 articles.
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3. Cabral, R., De La Torre, F., Costeira, J.P., Bernardino, A.: Unifying nuclear norm and bilinear factorization approaches for low-rank matrix decomposition. In: IEEE International Conference on Computer Vision, pp. 2488–2495 (2013)
4. Candès, E.J.S., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 1–73 (2011)
5. Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G.: Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process. Mag. 32(2), 145–163 (2014)
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