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Extreme learning machine has been widely used for its fast, efficient and good generalization ability in pattern recognition. But existing ELM and its improved algorithms do not adequately consider the impact of data dimension for classification performance and generalization ability of ELM, the redundant attributes and noise will reduce the generalization ability of ELM which contains when data dimension is too high. Aiming at this problem, this paper proposed a kind of extreme learning machine based on manifold learning. The algorithm combines dimensionality reduction technology, effectively eliminate the impact of data redundancy attribute and noise for classification performance of ELM. In order to verify the effectiveness of the proposed method, experimental use of widely used image data, experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM.