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. Author manuscript; available in PMC: 2019 May 15.
Published in final edited form as: Med Image Comput Comput Assist Interv. 2018 Sep 26;11070:647–654. doi: 10.1007/978-3-030-00928-1_73

Fig. 1.

Fig. 1.

Illustration of our multi-center low-rank representation learning method. There are I source domains and a target domain. Each source domain (denoted as Si) and the target domain (denoted as T) contain two samples (marked as triangles and circles) belonging to two categories. Our method transforms each source domain Si into an intermediate representation PiSi, and each transformed sample can be linearly represented by the target samples with a common latent projection (i.e., PT).