A graph representation approach for selecting the optimal projection in information fusion

Information fusion is a key research area widely applied to various multimedia analysis tasks such as artificial intelligence, humancomputer interaction, robotics, distributed computing, financial systems and security/surveillance. Feature level fusion has been considered as the most promising fusion method due to the rich information presented at this level. A critical operation of feature level fusion is the projection of the features onto a space which best presents the information for recognition. However, the identification of the optimal projection of the multimodal features onto the projected space remains a difficult task. This paper presents a graph representation approach for selecting optimal projection in information fusion which substantially minimizes the effort of finding the optimal or near-optimal dimension of the features in the projected space. The effectiveness of the proposed method is demonstrated through numerous experimentation on handwritten digit recognition and audio based emotion recognition problems.