  Singular-Value Decomposition (SVD) is a ubiquitous data analysis method in
engineering, science, and statistics. Singular-value estimation, in particular,
is of critical importance in an array of engineering applications, such as
channel estimation in communication systems, electromyography signal analysis,
and image compression, to name just a few. Conventional SVD of a data matrix
coincides with standard Principal-Component Analysis (PCA). The L2-norm (sum of
squared values) formulation of PCA promotes peripheral data points and, thus,
makes PCA sensitive against outliers. Naturally, SVD inherits this outlier
sensitivity. In this work, we present a novel robust non-parametric method for
SVD and singular-value estimation based on a L1-norm (sum of absolute values)
formulation, which we name L1-cSVD. Accordingly, the proposed method
demonstrates sturdy resistance against outliers and can facilitate more
reliable data analysis and processing in a wide range of engineering
applications.
