Sparse Probabilistic Relational Projection

Authors

  • Wu-Jun Li Shanghai Jiao Tong University
  • Dit-Yan Yeung Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v26i1.8307

Keywords:

Dimensionality Reduction, Sparse Learning, PCA, Relational Learning

Abstract

Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretability because each principal component is a linear combination of all the original variables. In this paper, we propose a novel model, called sparse probabilistic relational projection (SPRP), to learn a sparse projection matrix for relational dimensionality reduction. The sparsity in SPRP is achieved by imposing on the projection matrix a sparsity-inducing prior such as the Laplace prior or Jeffreys prior. We propose an expectation-maximization (EM) algorithm to learn the parameters of SPRP. Compared with PRPCA, the sparsity in SPRP not only makes the results more interpretable but also makes the projection operation much more efficient without compromising its accuracy. All these are verified by experiments conducted on several real applications.

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Published

2021-09-20

How to Cite

Li, W.-J., & Yeung, D.-Y. (2021). Sparse Probabilistic Relational Projection. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 1005-1011. https://doi.org/10.1609/aaai.v26i1.8307

Issue

Section

AAAI Technical Track: Machine Learning