Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion
ID:112 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:370 Oral Presentation

Start Time:2020-06-08 14:40(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS12 Structured Matrix/Tensor Decompositions: Models, Applications And Fast Algorithms

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Abstract
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. However, existing GRMF approaches often use the squared loss to measure the pairwise differences, which may be overly influenced by dissimilar pairs and lead to inferior prediction. To fully empower pairwise learning for matrix completion, we propose a general optimization framework that allows a rich class of (non-)convex pairwise penalty functions. A new and efficient algorithm is developed to solve the proposed optimization problem. We conduct extensive experiments on real recommender datasets to
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Speaker
Kaiyi Ji
The Ohio State University, USA

Submission Author
Kaiyi Ji The Ohio State University, USA
Jian Tan Alibaba Group & The Ohio State University, USA
Jinfeng Xu The University of Hong Kong, Hong Kong
Yuejie Chi Carnegie Mellon University, USA
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Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

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IEEE Signal Processing Society
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