Abstract
With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can re ect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are primely mapped into lower-dimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction.
Abstract (translated)
随着特征提取技术的发展，一个样本总是可以由位于高维空间中的多个特征来表示。多个特征可以反映同一个样本的各种视角，因此多个视图之间必须存在兼容和互补的信息。因此，将多个功能集成在一起以获得更好的性能是很自然的。但是，大多数多视图降维方法无法处理高维非线性空间的多个特征。为了解决这个问题，本文提出了一种新的多视图降维方法 - 多视图重建保持嵌入（MRPE）。 MRPE通过利用其k个最近邻居来重建每个样本。每个样本与其邻居之间的相似性被主要映射到较低维空间，以便保留原始流形的基础邻域结构。 MRPE通过线性重建充分利用来自多个视图的每个样本与其邻居之间的相关性。此外，MRPE构造优化问题并导出迭代过程以获得低维嵌入。基于文档分类，人脸识别和图像检索应用的各种评估证明了我们提出的方法在多视图降维方面的有效性。
URL
https://arxiv.org/abs/1807.10614
