稀疏约束下非负矩阵分解的增量学习算法

Incremental Learning Algorithm of Non-negative Matrix Factorization with Sparseness Constraints

非负矩阵分解(NMF)是一种有效的子空间降维方法。为了改善非负矩阵分解运算规模随训练样本增多而不断增大的现象,同时提高分解后数据的稀疏性,提出了一种稀疏约束下非负矩阵分解的增量学习算法,该算法在稀疏约束的条件下利用前一次分解的结果参与迭代运算,在节省大量运算时间的同时提高了分解后数据的稀疏性。在ORL和CBCL人脸数据库上的实验表明了该算法降维的有效性。

Non-negative matrix factorization is a useful method of subspace dimensionality reduction. However,with the increasing of training samples, the computing scale of non-negative matrix factorization increases rapidly. To solve this problem and improve the sparseness of the data obtained after factorization as well,an incremental learning algorithm of non-negative matrix factorization with sparseness constraints was proposed in this paper. Using the results of previous factorization involved in iterative computation with sparseness constraints, the cost of the computation is reduced and the sparseness of data after factorization is highly improved. Experimental results on both ORL and CBCL face databa- ses show that the pror^osed method is effective on dimensionality reduction.