Classification via Incoherent Subspaces

In this article we present a signal model for classification based on a collection of low dimensional subspaces embedded into the high dimensional signal space. We develop an alternate projection algorithm to find such a collection and finally test the classification performance of our scheme in comparison to Fisher's LDA and a recent approach based on sparse approximation.


Published in:
Rejecta Mathematica, 2, 1, 1-18
Year:
2011
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Note: The status of this file is: Involved Laboratories Only


 Record created 2009-02-17, last modified 2018-03-17

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