  Multi-view learning accomplishes the task objectives of classification by
leverag-ing the relationships between different views of the same object. Most
existing methods usually focus on consistency and complementarity between
multiple views. But not all of this information is useful for classification
tasks. Instead, it is the specific discriminating information that plays an
important role. Zhong Zhang et al. explore the discriminative and
non-discriminative information exist-ing in common and view-specific parts
among different views via joint non-negative matrix factorization. In this
paper, we improve this algorithm on this ba-sis by using the cross entropy loss
function to constrain the objective function better. At last, we implement
better classification effect than original on the same data sets and show its
superiority over many state-of-the-art algorithms.
