Multiview learning methods often achieve improvement compared with single-view-based approaches in many applications. Due to the powerful nonlinear ability and probabilistic perspective of Gaussian process (GP), some GP-based multiview efforts were presented. However, most of these methods make a strong assumption on the kernel function (e.g., radial basis function), which limits the capacity of the real data modeling. In order to address this issue, in this paper, we propose a novel multiview approach by combining a multikernel and GP latent variable model. Instead of designing a deterministic kernel function, multiple kernel functions are established to automatically adapt various types of data. Considering a simple way of obtaining latent variables at the testing stage, a projection from the observed space to the latent space as a back constraint has also been simultaneously introduced into the proposed method. Additionally, different from some existing methods which apply the classifiers off-line, a hinge loss is embedded into the model to jointly learn the classification hyperplane, encouraging the latent variables belonging to the different classes to be separated. An efficient algorithm based on the gradient decent technique is constructed to optimize our method. Finally, we apply the proposed approach to three real-world datasets and the associated results demonstrate the effectiveness and superiority of our model compared with other state-of-the-art methods.

中文翻译：

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多核共享高斯过程潜变量模型的视觉分类


在许多应用中，与基于单视图的方法相比，多视图学习方法通​​常会取得改进。由于高斯过程（GP）强大的非线性能力和概率视角，一些基于GP的多视图工作被提出。然而，这些方法大多数对核函数（例如径向基函数）做出了强有力的假设，这限制了真实数据建模的能力。为了解决这个问题，在本文中，我们结合多核和 GP 潜变量模型提出了一种新颖的多视图方法。不是设计确定性核函数，而是建立多个核函数来自动适应各种类型的数据。考虑到在测试阶段获得潜在变量的简单方法，从观察空间到潜在空间的投影作为后向约束也被同时引入到所提出的方法中。此外，与一些离线应用分类器的现有方法不同，模型中嵌入了铰链损失以共同学习分类超平面，从而鼓励分离属于不同类别的潜在变量。构建了一种基于梯度下降技术的有效算法来优化我们的方法。最后，我们将所提出的方法应用于三个现实世界的数据集，相关结果证明了我们的模型与其他最先进的方法相比的有效性和优越性。