Optimal algorithms for L1 -subspace signal processingOptimal algorithms for L1 -subspace signal processing Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-232014enWe describe ways to define and calculate L1-norm signal subspaces that are less sensitive to outlying data than L2-calculated subspaces. We start with the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D. We show that while the general problem is formally NP-hard in asymptotically large N, D, the case of engineering interest of fixed dimension D and asymptotically large sample size N is not. In particular, for the case where the sample size is less than the fixed dimension , we present in explicit form an optimal algorithm of computational cost 2N. For the case N ≥ D, we present an optimal algorithm of complexity O(ND). We generalize to multiple L1-max-projection components and present an explicit optimal L1 subspace calculation algorithm of complexity O(NDK-K+1) where K is the desired number of L1 principal components (subspace rank). We conclude with illustrations of L1-subspace signal processing in the fields of data dimensionality reduction, direction-of-arrival estimation, and image Δημοσίευση σε επιστημονικό περιοδικό http://creativecommons.org/licenses/by/4.0/IEEE Transactions on Signal Processing19625046 - 5058 Markopoulos Panagiotis Μαρκοπουλος Παναγιωτης Karystinos Georgios Καρυστινος Γεωργιος Pados, D.A Institute of Electrical and Electronics Engineers $L_{1}$ norm $L_{2}$ norm dimensionality reduction direction-of-arrival estimation eigendecomposition erroneous data faulty measurements machine learning outlier resistance subspace signal processing