Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

Citations Over Time

Abstract

Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Nevertheless, when training data is scarce their performance is significantly degraded due to overfitting. In this paper, we mitigate overfitting with a data-dependent regularization technique that relies on the principles of multi-task learning. Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task. The latter acts as an inductive bias, leading to solutions that generalize better. The proposed model outperforms the existing autoencoder-based models designed for matrix completion, achieving high reconstruction accuracy in well-known datasets.

Related Papers

• → Regularizing autoencoder-based matrix completion models via manifold learning(2018)6 cited
• → Regularization Learning for Image Recognition(2019)1 cited
• → Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing(2017)6 cited
• → Adversarially Approximated Autoencoder for Image Generation and Manipulation(2019)6 cited
• → Sparse Group Inductive Matrix Completion(2018)2 cited