  Only learning one projection matrix from original samples to the
corresponding binary labels is too strict and will consequentlly lose some
intrinsic geometric structures of data. In this paper, we propose a novel
transition subspace learning based least squares regression (TSL-LSR) model for
multicategory image classification. The main idea of TSL-LSR is to learn a
transition subspace between the original samples and binary labels to alleviate
the problem of overfitting caused by strict projection learning. Moreover, in
order to reflect the underlying low-rank structure of transition matrix and
learn more discriminative projection matrix, a low-rank constraint is added to
the transition subspace. Experimental results on several image datasets
demonstrate the effectiveness of the proposed TSL-LSR model in comparison with
state-of-the-art algorithms
