IEEE Trans Cybern. 2014 Dec;44(12):2806-19. doi: 10.1109/TCYB.2014.2317174. Epub 2014 May 5.
Current discriminant nonnegative matrix factorization (NMF) methods either do not guarantee convergence to a stationary limit point or assume a compact data distribution inside classes, thus ignoring intra class variance in extracting discriminant data samples representations. To address both limitations, we regard that data inside each class has a multimodal distribution, forming various subclasses and perform optimization using a projected gradients framework to ensure limit point stationarity. The proposed method combines appropriate clustering-based discriminant criteria in the NMF decomposition cost function, in order to find discriminant projections that enhance class separability in the reduced dimensional projection space, thus improving classification performance. The developed algorithms have been applied to facial expression, face and object recognition, and experimental results verified that they successfully identified discriminant parts, thus enhancing recognition performance.
当前的判别非负矩阵分解 (NMF) 方法要么不能保证收敛到稳定的极限点，要么假设类内数据分布紧凑，从而忽略了在提取判别数据样本表示时的类内方差。为了解决这两个限制，我们认为每个类内的数据具有多峰分布，形成各种子类，并使用投影梯度框架进行优化，以确保极限点的稳定性。所提出的方法将适当的基于聚类的判别准则结合到 NMF 分解代价函数中，以便找到判别投影，从而在降维投影空间中增强类可分离性，从而提高分类性能。所开发的算法已应用于面部表情、面部和物体识别，实验结果验证了它们能够成功识别判别部分，从而提高了识别性能。
