Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering ( i.e. , iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a $\ell _{1,2}$ -norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.

中文翻译：

---

iCmSC：不完整的跨模子空间聚类

跨模态聚类旨在将高度相似的跨模态数据聚为一组，同时将不相似的数据分开。尽管近年来开发了有希望的跨模式方法，但是当遇到不完整的跨模式数据时，现有的最新技术无法有效地捕获跨模式数据之间的相关性，这会严重降低聚类性能。为了很好地解决上述情况，我们提出了一种将规范相关分析和排他表示相结合的不完全交叉模态聚类方法，称为不完全交叉模态子空间聚类（ 即 ，iCmSC）。为了了解不完整跨模态数据之间的一致子空间表示，我们通过深度规范相关分析（DCCA）最大化了不同模态之间的内在相关性，而在DCCA的输出层之后提出了一个专有的自表达层。我们利用 $ \ ell _ {1,2} $ -在学习的子空间中对-范数进行正则化，以使学习的表示更具区分性，这使得不同聚类之间的样本互斥，并且同一聚类中的样本彼此有吸引力。同时，利用解码网络重构特征表示，并进一步保留原始交叉模态数据之间的结构信息。最后，我们通过广泛的实验证明了所提出的iCmSC的有效性，这可以证明iCmSC与最新技术相比始终如一地取得了很大的进步。