Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
| Autor: | Daniele Castellana, Davide Bacciu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Bayesian probability Probabilistic logic Machine Learning (stat.ML) Statistical model 02 engineering and technology Markov model 01 natural sciences Machine Learning (cs.LG) Interpretation (model theory) 010104 statistics & probability Tree (data structure) Matrix (mathematics) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Tensor 0101 mathematics Algorithm |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2019.8851851 |
| Popis: | Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-transition matrix. We propose a new approximation which lies on the Tucker factorisation of tensors. The probabilistic interpretation of such approximation allows us to define a new probabilistic model for tree-structured data. Hence, we define the new approximated model and we derive its learning algorithm. Then, we empirically assess the effective power of the new model evaluating it on two different tasks. In both cases, our model outperforms the other approximated model known in the literature. Comment: Accepted at IJCNN19 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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