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Linking between two data sources is a basic building block in numerous computer vision problems. In this paper, we set to answer a fundamental cognitive question: are prior correspondences necessary for linking between different domains? One of the most popular methods for linking between domains is Canonical Correlation Analysis (CCA). All current CCA algorithms require correspondences between the views. We introduce a new method Unsupervised Correlation Analysis (UCA), which requires no priordoi:10.1109/cvpr.2018.00350 dblp:conf/cvpr/HoshenW18 fatcat:hk6lccjsprggdnfgstffb2ngma