DMCA
Cluster Canonical Correlation Analysis
| Citations: | 1 - 0 self |
BibTeX
@MISC{Rasiwasia_clustercanonical,
author = {Nikhil Rasiwasia and Dhruv Mahajan and Vijay Mahadevan and Gaurav Aggarwal},
title = {Cluster Canonical Correlation Analysis},
year = {}
}
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Abstract
In this paper we present cluster canonical cor-relation analysis (cluster-CCA) for joint dimen-sionality reduction of two sets of data points. Unlike the standard pairwise correspondence be-tween the data points, in our problem each set is partitioned into multiple clusters or classes, where the class labels define correspondences be-tween the sets. Cluster-CCA is able to learn dis-criminant 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 cluster-CCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to stan-dard (K)CCA. By means of experimental evalu-ation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks. 1
Keyphrases
cluster canonical correlation analysis data point standard pairwise correspondence non-linear relationship present cluster canonical cor-relation analysis joint dimen-sionality reduction experimental evalu-ation kernel extension multiple cluster art performance dis-criminant low dimensional representation cross-modal retrieval task class label define correspondence learned space different class benchmark datasets
