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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14031 |
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| _version_ | 1866918532799791104 |
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| author | Augustine, Midhun T. Patil, Parag Bhushan, Mani Bhartiya, Sharad |
| author_facet | Augustine, Midhun T. Patil, Parag Bhushan, Mani Bhartiya, Sharad |
| contents | This paper presents an autoencoder with ordered variance (AEO), in which the conventional reconstruction loss is augmented by a variance-based regularization term that promotes an ordered structure within the latent space. In this structure, the latent variables are ordered by their variance computed over the training data, facilitating systematic determination of the latent space dimensionality. The AEO is further extended using residual networks, resulting in a ResNet-based AEO (RAEO). Both AEO and RAEO green lead to discovery of nonlinear relationships among variables in unlabeled datasets, thereby enabling unsupervised static model extraction. Theoretical contributions include formal guarantees on the ordering of latent variances. The practical utility of the framework is demonstrated through its application to the identification of nonlinear steady-state models and their use in real-time optimization, with a continuous stirred tank reactor process serving as a representative case study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discovering Nonlinear Static Relationships in Unlabeled Dataset using Autoencoder with Ordered Variance Augustine, Midhun T. Patil, Parag Bhushan, Mani Bhartiya, Sharad Systems and Control Machine Learning This paper presents an autoencoder with ordered variance (AEO), in which the conventional reconstruction loss is augmented by a variance-based regularization term that promotes an ordered structure within the latent space. In this structure, the latent variables are ordered by their variance computed over the training data, facilitating systematic determination of the latent space dimensionality. The AEO is further extended using residual networks, resulting in a ResNet-based AEO (RAEO). Both AEO and RAEO green lead to discovery of nonlinear relationships among variables in unlabeled datasets, thereby enabling unsupervised static model extraction. Theoretical contributions include formal guarantees on the ordering of latent variances. The practical utility of the framework is demonstrated through its application to the identification of nonlinear steady-state models and their use in real-time optimization, with a continuous stirred tank reactor process serving as a representative case study. |
| title | Discovering Nonlinear Static Relationships in Unlabeled Dataset using Autoencoder with Ordered Variance |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2402.14031 |