In this section, we discuss the concept of multi-modal learning. In this setting, correspondences are assumed to be on instance, rather then category, level. Also, here it is commonly assumed ample train **data** is available in both domains. Similarly to Sec. 5, the common goal of most methods is to estimate transformations L t nad L s so that P s ((X T L s = x|Y = k) = P t (X T L t = x|Y = k). This can be done by letting e.g. L s = I, thus mapping the target domain to the source domain, or vice versa. One could also consider mapping both spaces into a common space. We will begin this section by reviewing Canonical Correlation Analysis, Principal Component Analysis, Linear Discriminant Analysis. We then consider recent work utilizing these methods [Sharma et al., 2012].