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This paper presents a novel classifier based on collaborative representation and multiple onedimensional embedding with applications to face recognition. To use multiple 1-D embedding (1DME) framework in semi-supervised learning is first proposed by one of the authors, J. Wang, in 2014. The main idea of the multiple 1-D embedding is the following: Given a high-dimensional data set, we first map it onto several different 1-D sequences on the line while keeping the proximity of data points in thedoi:10.1142/s0219691316400038 fatcat:u7ah3wmd5jf25ahaja4njnrf5e