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Reducing the Rank of Relational Factorization Models by Including Observable Patterns
| Content Provider | CiteSeerX |
|---|---|
| Author | Nickel, Maximilian Jiang, Xueyan Tresp, Volker |
| Abstract | Tensor factorization has become a popular method for learning from multi-relational data. In this context, the rank of the factorization is an important parame-ter that determines runtime as well as generalization ability. To identify conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover adjacency tensors. Based on our findings, we propose a novel additive tensor factorization model to learn from latent and observable patterns on multi-relational data and present a scalable algorithm for computing the factorization. We show experimentally both that the proposed additive model does improve the predictive performance over pure latent variable methods and that it also reduces the required rank — and therefore runtime and memory complexity — significantly. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Observable Pattern Relational Factorization Model Multi-relational Data Therefore Runtime Memory Complexity Scalable Algorithm Adjacency Tensor Additive Model Tensor Factorization Generalization Ability Predictive Performance Popular Method Pure Latent Variable Method Novel Additive Tensor Factorization Model Important Parame-ter Required Rank Relational Data Efficient Approach |
| Content Type | Text |
