Manifold Regularized Multi-view Feature Selection for Web Image Annotation

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• 作者：Yangxi Li (21)
  Xin Shi (22)
  Lingling Tong (21)
  Yong Luo (22)
  Jinhui Tu (23)
  Xiaobo Zhu (21)
  
• 关键词：Feature selection ; multi ; task ; multi ; view ; manifold regularization
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8879
• 期：1
• 页码：103-112
• 全文大小：240 KB
• 参考文献：1. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research聽7, 2399鈥?434 (2006)
  2. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology聽2(3), 1鈥?7 (2011) CrossRef
  3. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from national university of singapore. In: ACM International Conference on Image and Video Retrieval (2009)
  4. Feng, Y., Xiao, J., Zhuang, Y., Liu, X.: Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol.聽7724, pp. 343鈥?57. Springer, Heidelberg (2013) CrossRef
  5. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507鈥?14 (2005)
  6. Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. In: ACM Multimedia, pp. 911鈥?20 (2006)
  7. Li, Z., Yang, Y., Liu, J., Zhou, X., Lu, H.: Unsupervised feature selection using nonnegative spectral analysis. In: AAAI Conference on Artificial Intelligence (2012)
  8. Luo, Y., Tao, D., Xu, C., Xu, C., Liu, H., Wen, Y.: Multiview vector-valued manifold regularization for multilabel image classification. IEEE Transactions on Neural Networks and Learning Systems聽24(5), 709鈥?22 (2013) CrossRef
  9. Molina, L.C., Belanche, L., Nebot, 脌.: Feature selection algorithms: A survey and experimental evaluation. In: International Conference on Data Mining, pp. 306鈥?13 (2002)
  10. Nie, F., Huang, H., Cai, X., Ding, C.: Efficient and robust feature selection via joint l2,1-norms minimization. In: Advances in Neural Information Processing Systems 23, pp. 1813鈥?821 (2010)
  11. Obozinski, G., Taskar, B., Jordan, M.: Multi-task feature selection. In: ICML Workshop on Structural Knowledge Transfer for Machine Learning (2006)
  12. Tang, J., Hu, X., Gao, H., Liu, H.: Unsupervised feature selection for multi-view data in social media. In: SIAM International Conference on Data Mining, pp. 270鈥?78 (2013)
  13. Xu, Z., King, I., Lyu, M.T., Jin, R.: Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks聽21(7), 1033鈥?047 (2010) CrossRef
  14. Yang, Y., Shen, H.T., Ma, Z., Huang, Z., Zhou, X.: l 2, 1-norm regularized discriminative feature selection for unsupervised learning. In: International Joint Conference on Artificial Intelligence, pp. 1589鈥?594 (2011)
  
• 作者单位：Yangxi Li (21)
  Xin Shi (22)
  Lingling Tong (21)
  Yong Luo (22)
  Jinhui Tu (23)
  Xiaobo Zhu (21)
  
  21. National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), China
  22. Key Laboratory of Machine Perception, Peking University, China
  23. China Construction Bank, Guangzhou, China
  
• ISSN：1611-3349

文摘

The features used in many multimedia analysis-based applications are frequently of very high dimension. Feature selection offers several advantages in highly dimensional cases. Recently, multi-task feature selection has attracted much attention, and has been shown to often outperform the traditional single-task feature selection. Current multi-task feature selection methods are either supervised or unsupervised. In this paper, we address the semi-supervised multi-task feature selection problem. We first introduce manifold regularization in multi-task feature selection to utilize the limited number of labeled samples and the relatively large amount of unlabeled samples. However, the graph constructed in manifold regularization from a single feature representation (view) may be unreliable. We thus propose to construct the graph using the heterogeneous feature representations from multiple views. The proposed method is called manifold regularized multi-view feature selection (MRMVFS), which can exploit the label information, label relationship, data distribution, as well as correlation among different kinds of features simultaneously to boost the feature selection performance. All these information are integrated into a unified learning framework to estimate feature selection matrix, as well as the adaptive view weights. Experimental results on a real-world web image dataset demonstrate the effectiveness and superiority of the proposed MRMVFS over other state-of-the-art feature selection methods.