可行方向算法与模拟退火结合的NMF特征提取方法

Learning NMF Representation Using a Hybrid Method Combining Feasible Direction Algorithm and Simulated Annealing

NMF子空间特征提取被表示成一个大规模线性约束非线性优化问题 .为了获得更优性能的基图像 ,设计了一个可行方向算法结合模拟退火算法的混合算法来求解这个优化问题 .以基于梯度的可行方向算法作为局部寻优的手段 ,加快收敛速度 ;以模拟退火算法作为全局寻优的手段 ,避免优化过程陷入局部极小点 .同时 ,在模拟退火操作中 ,采用对比度增强算法 ,使获得的基图像更加地空间局部化 .实验表明 ,本文的可行方向算法比采用归一化实现等式约束的原算法在学习的最后阶段有更好的收敛速度 ,所获得的基图像更加地空间局部化 。

Non-negative matrix factorization(NMF) is formulated as a large-scale optimization problem with linear equality constraints.To get better performance,a hybrid combining simulated annealing(SA) and gradient-based algorithm is designed.The simulated annealing gradually produces better solutions with the gradient-based algorithm serving as an 'accelerator'.Experimental results are presented to compare our method and the original method for learning NMF representation,which demonstrate the proposed method can learn basis images more spatially localized and perform better than the original one at later training stages.The comparison study on face reconstruction also shows that the proposed method leads to better results than PCA and the original NMF.