Logistic Tensor Factorization for Multi-Relational Data
Machine Learning
2013-06-11 v1 Machine Learning
Abstract
Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly.
Cite
@article{arxiv.1306.2084,
title = {Logistic Tensor Factorization for Multi-Relational Data},
author = {Maximilian Nickel and Volker Tresp},
journal= {arXiv preprint arXiv:1306.2084},
year = {2013}
}
Comments
Accepted at ICML 2013 Workshop "Structured Learning: Inferring Graphs from Structured and Unstructured Inputs" (SLG 2013)