Incremental Learning of Latent Forests
In the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily interpretable. In this paper, we propose two increme...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9207730/ |
| _version_ | 1818662271999541248 |
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| author | Fernando Rodriguez-Sanchez Pedro Larranaga Concha Bielza |
| author_facet | Fernando Rodriguez-Sanchez Pedro Larranaga Concha Bielza |
| author_sort | Fernando Rodriguez-Sanchez |
| collection | DOAJ |
| description | In the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily interpretable. In this paper, we propose two incremental algorithms for learning forests of latent trees. Unlike current methods, the proposed algorithms are based on the variational Bayesian framework, which allows them to introduce uncertainty into the learning process and work with mixed data. The first algorithm, incremental learner, determines the forest structure and the cardinality of its latent variables in an iterative search process. The second algorithm, constrained incremental learner, modifies the previous method by considering only a subset of the most prominent structures in each step of the search. Although restricting each iteration to a fixed number of candidate models limits the search space, we demonstrate that the second algorithm returns almost identical results for a small fraction of the computational cost. We compare our algorithms with existing methods by conducting a comparative study using both discrete and continuous real-world data. In addition, we demonstrate the effectiveness of the proposed algorithms by applying them to data from the 2018 Spanish Living Conditions Survey. All code, data, and results are available at https://github.com/ferjorosa/incremental-latent-forests. |
| first_indexed | 2024-12-17T04:58:19Z |
| format | Article |
| id | doaj.art-6407a10f012b40edb94f6cd91234c219 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T04:58:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6407a10f012b40edb94f6cd91234c2192022-12-21T22:02:38ZengIEEEIEEE Access2169-35362020-01-01822442022443210.1109/ACCESS.2020.30270649207730Incremental Learning of Latent ForestsFernando Rodriguez-Sanchez0https://orcid.org/0000-0003-0790-6983Pedro Larranaga1https://orcid.org/0000-0003-0652-9872Concha Bielza2Departamento de Inteligencia Artificial, Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, SpainDepartamento de Inteligencia Artificial, Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, SpainDepartamento de Inteligencia Artificial, Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, SpainIn the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily interpretable. In this paper, we propose two incremental algorithms for learning forests of latent trees. Unlike current methods, the proposed algorithms are based on the variational Bayesian framework, which allows them to introduce uncertainty into the learning process and work with mixed data. The first algorithm, incremental learner, determines the forest structure and the cardinality of its latent variables in an iterative search process. The second algorithm, constrained incremental learner, modifies the previous method by considering only a subset of the most prominent structures in each step of the search. Although restricting each iteration to a fixed number of candidate models limits the search space, we demonstrate that the second algorithm returns almost identical results for a small fraction of the computational cost. We compare our algorithms with existing methods by conducting a comparative study using both discrete and continuous real-world data. In addition, we demonstrate the effectiveness of the proposed algorithms by applying them to data from the 2018 Spanish Living Conditions Survey. All code, data, and results are available at https://github.com/ferjorosa/incremental-latent-forests.https://ieeexplore.ieee.org/document/9207730/Latent variable modelvariational Bayeslatent tree modelhidden variables |
| spellingShingle | Fernando Rodriguez-Sanchez Pedro Larranaga Concha Bielza Incremental Learning of Latent Forests IEEE Access Latent variable model variational Bayes latent tree model hidden variables |
| title | Incremental Learning of Latent Forests |
| title_full | Incremental Learning of Latent Forests |
| title_fullStr | Incremental Learning of Latent Forests |
| title_full_unstemmed | Incremental Learning of Latent Forests |
| title_short | Incremental Learning of Latent Forests |
| title_sort | incremental learning of latent forests |
| topic | Latent variable model variational Bayes latent tree model hidden variables |
| url | https://ieeexplore.ieee.org/document/9207730/ |
| work_keys_str_mv | AT fernandorodriguezsanchez incrementallearningoflatentforests AT pedrolarranaga incrementallearningoflatentforests AT conchabielza incrementallearningoflatentforests |