School of Information Science and Engineering, Yunnan University, Kunming, 650500, China.
Neural Netw. 2019 May;113:102-115. doi: 10.1016/j.neunet.2018.11.003. Epub 2019 Jan 11.
Dimensionality reduction has obtained increasing attention in the machine learning and computer vision communities due to the curse of dimensionality. Many manifold embedding methods have been proposed for dimensionality reduction. Many of them are supervised and based on graph regularization whose weight affinity is determined by original noiseless data. When data are noisy, their performance may degrade. To address this issue, we present a novel unsupervised robust discriminative manifold embedding approach called URDME, which aims to offer a joint framework of dimensionality reduction, discriminative subspace learning , robust affinity representation and discriminative manifold embedding. The learned robust affinity not only captures the global geometry and intrinsic structure of underlying high-dimensional data, but also satisfies the self-expressiveness property. In addition, the learned projection matrix owns discriminative ability in the low-dimensional subspace. Experimental results on several public benchmark datasets corroborate the effectiveness of our approach and show its competitive performance compared with the related methods.
降维在机器学习和计算机视觉领域受到越来越多的关注，因为维度的诅咒。已经提出了许多流形嵌入方法来进行降维。其中许多是有监督的，并且基于图正则化，其权重亲和力由原始无噪声数据确定。当数据有噪声时，它们的性能可能会下降。为了解决这个问题，我们提出了一种新的无监督鲁棒判别流形嵌入方法，称为 URDME，它旨在提供一个联合框架的降维，判别子空间学习，鲁棒相似性表示和判别流形嵌入。学习到的鲁棒相似性不仅捕获了底层高维数据的全局几何形状和内在结构，而且满足自表达性质。此外，学习到的投影矩阵在低维子空间中具有判别能力。在几个公共基准数据集上的实验结果证实了我们的方法的有效性，并显示了与相关方法相比的竞争性能。
