Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations
This work addresses the need for better distance metrics in applications like image retrieval and face recognition by handling multiview data, though it appears incremental as it builds on existing multiview metric learning methods.
The paper tackles the problem of learning a distance metric for multiview data by proposing a self-weighted algorithm that maximizes cross-correlations between views, achieving improved performance on benchmark datasets.
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.