SPARSE CANONICAL CORRELATION ANALYSIS FOR MOBILE MEDIA RECOGNITION ON THE CLOUD

Authors

  • YANJIANG WANG College of Information and Control Engineering, China University of Petroleum #66 Changjiang West Road, Huangdao District, Qingdao, Shandong 266580, China
  • BIN ZHOU Shandong Wide Area Technology Co.,Ltd. Dongying, Shandong 257081, China
  • WEIFENG LIU College of Information and Control Engineering, China University of Petroleum Qingdao, Shandong 266580, China
  • HUIMIN ZHANG College of Information and Control Engineering, China University of Petroleum Qingdao, Shandong 266580, China

Keywords:

Canonical correlation analysis (CCA), sparse canonical correlation analysis (SCCA), power method, mobile media

Abstract

With the rapid development of the Internet technology and smartphone, people can easily capture and upload media information including text, audio, photos, and video. And then it becomes one critical demand to effectively and efficiently manage these personal multimedia that are often presented in multiple modalities. Canonical correlation analysis (CCA) has been widely employed for multi-modal data in many applications because of its promising performance in feature extraction and subspace learning for multivariate vectors. However, the traditional CCA may be difficult to interpret especially when the original variables are expected to involve only a few components. In this paper, we develop a mobile media recognition method on the cloud. Particularly, we propose sparse canonical correlation analysis (SCCA) on the cloud. SCCA can find a reasonable trade-off between statistical fidelity and interpretability. Furthermore, we employ a generalised power method to optimise the SCCA algorithm. Finally, we conduct extensive experiments for recognition on several popular databases including UCI datasets and USAA dataset. Experimental results demonstrate that the proposed SCCA algorithm outperforms the traditional CCA algorithm.

 

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Published

2017-07-30

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