IEEE Trans Pattern Anal Mach Intell. 2023 Aug;45(8):9552-9566. doi: 10.1109/TPAMI.2023.3253211. Epub 2023 Jun 30.
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel. However, existing approaches compute the kernels independently for each view. This ignores complementary information across views and thus may result in a bad kernel choice. In contrast, we propose the Contrastive Multi-view Kernel - a novel kernel function based on the emerging contrastive learning framework. The Contrastive Multi-view Kernel implicitly embeds the views into a joint semantic space where all of them resemble each other while promoting to learn diverse views. We validate the method's effectiveness in a large empirical study. It is worth noting that the proposed kernel functions share the types and parameters with traditional ones, making them fully compatible with existing kernel theory and application. On this basis, we also propose a contrastive multi-view clustering framework and instantiate it with multiple kernel k-means, achieving a promising performance. To the best of our knowledge, this is the first attempt to explore kernel generation in multi-view setting and the first approach to use contrastive learning for a multi-view kernel learning.
核方法是多视图学习中一种经过验证的技术。它隐式地定义了一个 Hilbert 空间，在这个空间中可以对样本进行线性分离。大多数基于核的多视图学习算法计算一个核函数，将视图聚合和压缩到单个核中。然而，现有的方法为每个视图独立地计算核函数。这忽略了视图之间的互补信息，因此可能导致核函数选择不佳。相比之下，我们提出了对比多视图核函数（Contrastive Multi-view Kernel）——一种基于新兴对比学习框架的新核函数。对比多视图核函数隐式地将视图嵌入到一个联合语义空间中，在这个空间中，所有视图彼此相似，同时促进学习多样化的视图。我们在一个大规模的实证研究中验证了该方法的有效性。值得注意的是，所提出的核函数与传统核函数具有相同的类型和参数，因此完全兼容现有的核理论和应用。在此基础上，我们还提出了一个对比多视图聚类框架，并将其实例化为多核 k-均值，取得了有前景的性能。据我们所知，这是首次尝试在多视图设置中探索核生成，也是首次将对比学习应用于多视图核学习。
