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Clustering adaptive canonical correlations for high-dimensional multi-modal data
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2020-04-19 , DOI: 10.1016/j.jvcir.2020.102815
Shuzhi Su , Xianjin Fang , Gaoming Yang , Bin Ge , Ping Zheng

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.



中文翻译:

聚类高维多模态数据的自适应规范相关性

多模式规范相关分析(MCCA)是一种重要的联合降维方法,已广泛应用于多模式数据的聚类任务。基于MCCA的聚类通常是对高维数据进行降维,然后对低维数据进行聚类。但是,两阶段聚类很难保证降维和聚类的适应性,这将影响最终的聚类性能。为了解决这个问题,我们提出了一种新的聚类自适应多模态规范相关性(CAMCC)方法,该方法构造了一个多模态相关性学习和聚类的统一优化模型。该方法不仅实现了在无监督情况下的相关投影方向的判别学习,而且能够直接获得多模态数据的分类标签。另外,该方法还实现了类标签中的样本外扩展。通过迭代方式优化CAMCC的解决方案,并分析其收敛性。在各种数据集上的大量实验结果证明了该方法的有效性。

更新日期:2020-04-19
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