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 ℓ 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：

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iCmSC：不完整的跨模态子空间聚类


跨模态聚类旨在将高度相似的跨模态数据聚类为一组，同时分离不相似的数据。尽管近年来发展了有前途的跨模态方法，但现有的最先进技术在遇到不完整的跨模态数据时无法有效捕获跨模态数据之间的相关性，这会严重降低聚类性能。为了很好地解决上述情况，我们提出了一种集成典型相关分析和排他表示的新型不完全跨模态聚类方法，称为不完全跨模态子空间聚类（即iCmSC）。为了学习不完整的跨模态数据之间一致的子空间表示，我们通过深度典型相关分析（DCCA）最大化不同模态之间的内在相关性，同时在 DCCA 的输出层之后提出了一个专有的自我表达层。我们在学习的子空间中利用 ℓ 1,2 范数正则化来使学习的表示更具辨别力，这使得不同簇之间的样本相互排斥，而同一簇之间的样本相互吸引。同时，采用解码网络来重建特征表示，并进一步保留原始跨模态数据中的结构信息。最后，我们通过大量实验证明了所提出的 iCmSC 的有效性，这可以证明 iCmSC 与最先进的技术相比始终取得了巨大的进步。