Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion

16 Feb 2018 Kaiyi Ji Jian Tan Jinfeng Xu Yuejie Chi

Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to improve prediction performance, the similarities between latent variables can be exploited by pairwise learning using the graph regularized matrix factorization (GRMF) method... (read more)

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