Traditional NMF method does not fully utilize the internal similarity among original data, thus the performance of dimensionality reduction is limited. To this end, a new nonnegative matrix facto rization algorithm restrained by the regularization of potential information is proposed. Firstly, the potential information is mined via the iterative nearest neighbor. Then the potential information is utilized to construct similarity graph of data set. Finally, the similarity graph is incorporated as a regularization term to preserve the relationship between original data in the decomposition process of nonnegative matrix. The regularization term keeps the similarity between the original data in the process of dimensionality reduction, which can improve the discriminant ability of nonnegative matrix factorization algorithm. Thorough experiments on standard image databases show the superior performance of the proposed method. Traditional NMF method does not fully utilize the internal similarity among original data, thus the performance of dimensionality reduction is limited. To this end, a new nonnegative matrix facto rization algorithm restrained by the regularization of potential information is proposed. Firstly, the potential information is mined via the iterative nearest neighbor. Then the potential information is utilized to construct similarity graph of data set. Finally, the similarity graph is incorporated as a regularization term to preserve the relationship between original data in the decomposition process of nonnegative matrix. The regularization term keeps the similarity between the original data in the process of dimensionality reduction, which can improve the discriminant ability of nonnegative matrix factorization algorithm. Thorough experiments on standard image databases show the superior performance of the proposed method. Traditional NMF method does not fully utilize the internal similarity among original data, thus the performance of dimensionality reduction is limited. To this end, a new nonnegative matrix facto rization algorithm restrained by the regularization of potential information is proposed. Firstly, the potential information is mined via the iterative nearest neighbor. Then the potential information is utilized to construct similarity graph of data set. Finally, the similarity graph is incorporated as a regularization term to preserve the relationship between original data in the decomposition process of nonnegative matrix. The regularization term keeps the similarity between the original data in the process of dimensionality reduction, which can improve the discriminant ability of nonnegative matrix factorization algorithm. Thorough experiments on standard image databases show the superior performance of the proposed method.