  With the development of multimedia time, one sample can always be described
from multiple views which contain compatible and complementary information.
Most algorithms cannot take information from multiple views into considerations
and fail to achieve desirable performance in most situations. For many
applications, such as image retrieval, face recognition, etc., an appropriate
distance metric can better reflect the similarities between various samples.
Therefore, how to construct a good distance metric learning methods which can
deal with multiview data has been an important topic during the last decade. In
this paper, we proposed a novel algorithm named Self-weighted Multiview Metric
Learning (SM2L) which can finish this task by maximizing the cross correlations
between different views. Furthermore, because multiple views have different
contributions to the learning procedure of SM2L, we adopt a self-weighted
learning framework to assign multiple views with different weights. Various
experiments on benchmark datasets can verify the performance of our proposed
method.
