Zhang Tengyu, Zeng Deyu, Liu Wei, Wu Zongze, Ding Chris, Zhong Xiaopin
School of Software Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China; College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China.
Neural Netw. 2025 May;185:107105. doi: 10.1016/j.neunet.2024.107105. Epub 2025 Jan 17.
Multi-view classification integrates features from different views to optimize classification performance. Most of the existing works typically utilize semantic information to achieve view fusion but neglect the spatial information of data itself, which accommodates data representation with correlation information and is proven to be an essential aspect. Thus robust independent subspace analysis network, optimized by sparse and soft orthogonal optimization, is first proposed to extract the latent spatial information of multi-view data with subspace bases. Building on this, a novel contrastive independent subspace analysis framework for multi-view classification is developed to further optimize from spatial perspective. Specifically, contrastive subspace optimization separates the subspaces, thereby enhancing their representational capacity. Whilst contrastive fusion optimization aims at building cross-view subspace correlations and forms a non overlapping data representation. In k-fold validation experiments, MvCISA achieved state-of-the-art accuracies of 76.95%, 98.50%, 93.33% and 88.24% on four benchmark multi-view datasets, significantly outperforming the second-best method by 8.57%, 0.25%, 1.66% and 5.96% in accuracy. And visualization experiments demonstrate the effectiveness of the subspace and feature space optimization, also indicating their promising potential for other downstream tasks. Our code is available at https://github.com/raRn0y/MvCISA.
多视图分类集成来自不同视图的特征以优化分类性能。大多数现有工作通常利用语义信息来实现视图融合，但忽略了数据本身的空间信息，而空间信息包含具有相关信息的数据表示，并且已被证明是一个重要方面。因此，首先提出了通过稀疏和软正交优化进行优化的鲁棒独立子空间分析网络，以用子空间基提取多视图数据的潜在空间信息。在此基础上，开发了一种用于多视图分类的新颖对比独立子空间分析框架，以从空间角度进一步优化。具体而言，对比子空间优化分离子空间，从而增强其表示能力。而对比融合优化旨在建立跨视图子空间相关性并形成不重叠的数据表示。在k折验证实验中，MvCISA在四个基准多视图数据集上分别达到了76.95%、98.50%、93.33%和88.24%的最优准确率，在准确率上显著超过次优方法8.57%、0.25%、1.66%和5.96%。可视化实验证明了子空间和特征空间优化的有效性，也表明了它们在其他下游任务中的潜在应用前景。我们的代码可在https://github.com/raRn0y/MvCISA获取。
