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This paper describes a new video event detection framework based on subspace selection technique. With the approach, feature vectors presenting different kinds of video information can be easily projected from different modalities onto an unified subspace, on which recognition process can be performed. The approach is capable of discriminating different classes and preserving the intra-modal geometry of samples within an identical class. Distinguished from the existing multi-modal detection methods, the new system works well when some modalities are not available. Experimental results based on soccer video and TRECVID news video collections demonstrate the effectiveness, efficiency and robustness of the proposed method for individual recognition tasks in comparison to the existing approaches.