论文标题
随机内核多观看判别分析
Randomized Kernel Multi-view Discriminant Analysis
论文作者
论文摘要
在许多人工智能和计算机视觉系统中，可以在不同的观点或不同的传感器上观察到相同的对象，这增加了识别来自不同，甚至异质观点的对象的挑战。多观看判别分析（MVDA）是一种有效的多视图子空间学习方法，它通过以非双重方式从多个视图中共同学习多个观点识别的多个特定视图的线性预测来找到一个判别的共同子空间。在本文中，我们提出了多视图判别分析的内核版本，称为内核多视图判别分析（KMVDA）。为了克服众所周知的内核方法计算瓶颈，我们还研究了使用随机傅立叶特征（RFF）在kmvda中近似高斯内核的性能，以进行大规模学习。开发了有关稳定性的理论分析。我们还对几个流行的多视图数据集进行了实验，以说明我们提出的策略的有效性。
In many artificial intelligence and computer vision systems, the same object can be observed at distinct viewpoints or by diverse sensors, which raises the challenges for recognizing objects from different, even heterogeneous views. Multi-view discriminant analysis (MvDA) is an effective multi-view subspace learning method, which finds a discriminant common subspace by jointly learning multiple view-specific linear projections for object recognition from multiple views, in a non-pairwise way. In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA). To overcome the well-known computational bottleneck of kernel methods, we also study the performance of using random Fourier features (RFF) to approximate Gaussian kernels in KMvDA, for large scale learning. Theoretical analysis on stability of this approximation is developed. We also conduct experiments on several popular multi-view datasets to illustrate the effectiveness of our proposed strategy.
