The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However, most of existing works focus on learning a latent subspace but the semantically structural information is not well preserved. Thus, these approaches cannot get desired results. In this paper, we propose a novel framework, termed Cross-modal subspace learning via Kernel correlation maximization and Discriminative structure-preserving (CKD), to solve this problem in two aspects. Firstly, we construct a shared semantic graph to make each modality data preserve the neighbor relationship semantically. Secondly, we introduce the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency between feature-similarity and semantic-similarity of samples. Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality. The extensive experiments are performed to evaluate the proposed framework on the three public datasets. The experimental results demonstrated that the proposed CKD is competitive compared with the classic subspace learning methods.

中文翻译：

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通过核相关最大化和判别结构保留的跨模态子空间学习

异构数据之间的度量仍然是一个悬而未决的问题。已经开发了许多研究工作来学习一个公共子空间，其中可以直接计算不同模态之间的相似性。然而，现有的大多数工作都集中在学习潜在子空间，但语义结构信息并没有得到很好的保留。因此，这些方法不能得到预期的结果。在本文中，我们提出了一个新的框架，称为通过核相关最大化和判别结构保留（CKD）的跨模态子空间学习，从两个方面解决这个问题。首先，我们构建了一个共享语义图，使每个模态数据在语义上保持邻居关系。第二，我们引入了希尔伯特-施密特独立准则（HSIC）来确保样本的特征相似性和语义相似性之间的一致性。我们的模型不仅通过最大化核相关性来考虑模态间的相关性，而且还保留了每个模态内的语义结构信息。进行了广泛的实验以评估在三个公共数据集上提出的框架。实验结果表明，与经典的子空间学习方法相比，所提出的 CKD 具有竞争力。进行了广泛的实验以评估在三个公共数据集上提出的框架。实验结果表明，与经典的子空间学习方法相比，所提出的 CKD 具有竞争力。进行了广泛的实验以评估在三个公共数据集上提出的框架。实验结果表明，与经典的子空间学习方法相比，所提出的 CKD 具有竞争力。