L1范数约束正交子空间非负矩阵分解

Non-Negative Matrix Factorization on Orthogonal Subspace with L1 Norm Constrains

针对非负矩阵分解(NMF)相对稀疏或局部化描述原数据时导致的稀疏能力和程度比较弱的问题,提出了L1范数约束正交子空间非负矩阵分解方法.通过将L1范数约束引入到正交子空间非负矩阵分解的目标函数中,提升了分解结果的稀疏性.同时给出累乘迭代规则.在UCI、ORL和Yale三个数据库上进行的实验结果表明,该算法在聚类效果以及稀疏表达方面优于其他算法.

In order to solve the problem of unstable sparseness of Non-negative Matrix Factorization（NMF）, an improved NMF on orthogonal subspace with L1 norm constraints was proposed. L1 norm constrained was introduced into the objective function of NMF on Orthogonal Subspace（NMFOS）, which enhanced the sparsity of the decomposition results.The multiplicative updating procedure was also produced. Experiments on UCI, ORL, and Yale show that this algorithm is superior to other algorithms in clustering and sparse representation.