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While much research in human re-identification has focused on the single-shot case, in real-world applications we are likely to have an image sequence from both the person to be matched and each candidate in the gallery, extracted from automated video tracking. It is desirable to take advantage of the multiple visual aspects (states) of each subject observed during training and testing. However, since each subject may spend different amounts of time in each state, equally weighting all the images in a sequence is likely to produce suboptimal performance. To address this problem, we introduce an algorithm to hierarchically cluster image sequences and use the representative data samples to learn a feature subspace maximizing the Fisher criterion. The clustering and subspace learning processes are applied iteratively to obtain diversity-preserving discriminative features. A metric learning step is then applied to bridge the appearance difference between two cameras. The proposed method is evaluated on three multi-shot re-id datasets and the results outperform state-of-the-art methods.