A joint-space model for cross-lingual distributed representations generalizes language-invariant semantic features. In this paper, we present a matrix co-factorization framework for learning cross-lingual word embeddings. We explicitly define monolingual training objectives in the form of matrix decomposition, and induce cross-lingual constraints for simultaneously factorizing monolingual matrices. The cross-lingual constraints can be derived from parallel corpora, with or without word alignments. Empirical results on a task of cross-lingual document classification show that our method is effective to encode cross-lingual knowledge as constraints for cross-lingual word embeddings.