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In this paper a new approach to video event detection is presented, combining visual concept detection scores with a new dimensionality reduction technique. Specifically, a video is first decomposed to a sequence of shots, and trained visual concept detectors are used to represent video content with model vector sequences. Subsequently, an improved subclass discriminant analysis method is used to derive a concept subspace for detecting and recognizing high-level events. In this space, the median Hausdorff distance is used to implicitly align and compare event videos of different lengths, and the nearest neighbor rule is used for recognizing the event depicted in the video. Evaluation results obtained by our participation in the Multimedia Event Detection Task of the TRECVID 2010 competition verify the effectiveness of the proposed approach for event detection and recognition in large scale video collections.