During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it’s not accessible for these DR methods to achieve satisfactory performance when dealing with the features from multiple views. For multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly relevant but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity consensus between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other, then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with some famous methods.

在过去的几十年中，学习具有区分性信息以进行降维（DR）的低维空间引起了人们的兴趣。但是，当从多个视图处理功能时，这些DR方法无法获得令人满意的性能。对于多视图学习问题，一个实例可以由多个异构特征表示，这些特征高度相关，但有时彼此看起来有所不同。另外，来自多个视图的特征之间的相关性总是变化很大，这挑战了多视图学习方法的能力。因此，构建具有泛化性和可伸缩性的多视图学习框架非常必要，但是具有挑战性，它可以尽可能多地利用多视图信息。为了实现上述目标，本文提出了一种基于相似度共识的新颖的多视图学习框架，该框架在考虑框架可扩展性和鲁棒性的同时，充分利用了多视图特征之间的相关性。它旨在通过保留不同视图之间的相似性共识以捕获低维嵌入，将现有的DR方法直接扩展到多视图学习领域。分别提出了基于成对共识和质心共识的两种方案，以迫使多个视图相互学习，然后提出一种迭代交替策略，以获得最优解。在5个基准数据集上对提出的方法进行了评估，综合实验表明，我们提出的多视图框架可以通过一些著名的方法产生可比的和有希望的性能。