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Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

open access: yesIEEE Signal Processing Magazine, 2018
PCA is one of the most widely used dimension reduction techniques. A related easier problem is "subspace learning" or "subspace estimation". Given relatively clean data, both are easily solved via singular value decomposition (SVD).
Namrata Vaswani   +2 more
exaly   +5 more sources
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SUPERCYCLIC SUBSPACES

Bulletin of the London Mathematical Society, 2003
This survey describes some recent results on linear subspaces in which all elements, except the zero vector, are supercyclic for a given supercyclic operator. Before stating some results precisely, we need to recall that the set of normal eigenvalues of an operator \(T\), denoted by \(\sigma_0(T)\), is the set of isolated points in the spectrum of \(T\)
Montes-Rodríguez, Alfonso   +1 more
openaire   +2 more sources

Subspace condensation

ACM Transactions on Graphics, 2015
Subspace deformable body simulations can be very fast, but can behave unrealistically when behaviors outside the prescribed subspace such as novel external collisions, are encountered. We address this limitation by presenting a fast, flexible new method that allows full space computation to be activated in the neighborhood of novel events ...
Teng, Yun   +3 more
openaire   +2 more sources

Subspace

Proceedings of the 16th international conference on World Wide Web, 2007
Combining data and code from third-party sources has enabled a new wave of web mashups that add creativity and functionality to web applications. However, browsers are poorly designed to pass data between domains, often forcing web developers to abandon security in the name of functionality.
Collin Jackson, Helen J. Wang
openaire   +1 more source

Is a subspace containing a splitting subspace a splitting subspace?

Statistics & Probability Letters, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

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