  The Nearest subspace classifier (NSS) finds an estimation of the underlying
subspace within each class and assigns data points to the class that
corresponds to its nearest subspace. This paper mainly studies how well NSS can
be generalized to new samples. It is proved that NSS is strongly consistent
under certain assumptions. For completeness, NSS is evaluated through
experiments on various simulated and real data sets, in comparison with some
other linear model based classifiers. It is also shown that NSS can obtain
effective classification results and is very efficient, especially for large
scale data sets.
