Abstract
With the development of information technology, we have witnessed an age of data explosion which produces a large variety of data filled with redundant information. Because dimension reduction is an essential tool which embeds high-dimensional data into a lower-dimensional subspace to avoid redundant information, it has attracted interests from researchers all over the world. However, facing with features from multiple views, it's difficult for most dimension reduction methods to fully comprehended multi-view features and integrate compatible and complementary information from these features to construct low-dimensional subspace directly. Furthermore, most multi-view dimension reduction methods cannot handle features from nonlinear spaces with high dimensions. Therefore, how to construct a multi-view dimension reduction methods which can deal with multi-view features from high-dimensional nonlinear space is of vital importance but challenging. In order to address this problem, we proposed a novel method named Co-regularized Multi-view Sparse Reconstruction Embedding (CMSRE) in this paper. By exploiting correlations of sparse reconstruction from multiple views, CMSRE is able to learn local sparse structures of nonlinear manifolds from multiple views and constructs significative low-dimensional representations for them. Due to the proposed co-regularized scheme, correlations of sparse reconstructions from multiple views are preserved by CMSRE as much as possible. Furthermore, sparse representation produces more meaningful correlations between features from each single view, which helps CMSRE to gain better performances. Various evaluations based on the applications of document classification, face recognition and image retrieval can demonstrate the effectiveness of the proposed approach on multi-view dimension reduction.
Abstract (translated)
随着信息技术的发展，我们见证了一个数据爆炸的时代，它产生了大量的冗余信息数据。由于维数约简是将高维数据嵌入低维子空间以避免冗余信息的重要工具，引起了世界各国研究人员的兴趣。然而，面对多视图特征，大多数降维方法很难完全理解多视图特征，并从这些特征中整合兼容互补信息，直接构造低维子空间。此外，大多数多视图降维方法都不能处理高维非线性空间的特征。因此，如何构造一种能从高维非线性空间中处理多视角特征的多视角降维方法具有重要的意义和挑战性。为了解决这一问题，本文提出了一种新的共正则多视图稀疏重建嵌入方法。利用多视图稀疏重构的相关性，CMSRE能够从多视图中学习非线性流形的局部稀疏结构，并为其构造有意义的低维表示。由于所提出的共正则化方案，CMSRE尽可能地保留了多视图稀疏重建的相关性。此外，稀疏表示在每个视图的特征之间产生更有意义的关联，这有助于CMSRE获得更好的性能。基于文档分类、人脸识别和图像检索等应用的各种评价可以证明该方法在多视图降维中的有效性。
URL
https://arxiv.org/abs/1904.08499
