Cluster Canonical Correlation Analysis
Cluster Canonical Correlation Analysis
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发表时间：
2014-04
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通讯作者：
Nikhil Rasiwasia;D. Mahajan;V. Mahadevan;Gaurav Aggarwal
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作者：
Nikhil Rasiwasia;D. Mahajan;V. Mahadevan;Gaurav Aggarwal
在本文中，我们提出了聚类典型相关分析（cluster-CCA）的联合降维的两组数据点。与数据点之间的标准成对对应不同，在我们的问题中，每个集合被划分为多个聚类或类，其中类标签定义了集合之间的对应关系。CCA能够学习判别式低维表示，最大化两个集合之间的相关性，同时隔离学习空间上的不同类别。此外，我们提出了一个内核扩展，内核集群典型相关分析（cluster-KCCA），扩展clusterCCA占非线性关系。聚类-（K）CCA被证明是计算高效的，复杂度类似于标准（K）CCA。通过对基准数据集的实验评估，表明聚类-（K）CCA在跨模态检索任务中具有最佳性能。
In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, wheretheclass labelsdefinecorrespondencesbetween the sets. Cluster-CCA is able to learn discriminant low dimensional representations that maximize the correlation between the two sets while segregating the different classes on the learned space. Furthermore, we present a kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) that extends clusterCCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to standard (K)CCA. By means of experimental evaluation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks.