CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916682768842752 |
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| author | Byrski, Krzysztof Tabor, Jacek Spurek, Przemysław Mazur, Marcin |
| author_facet | Byrski, Krzysztof Tabor, Jacek Spurek, Przemysław Mazur, Marcin |
| contents | In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the univariate distribution, which is typically Gaussian, is suboptimal because the mean may be situated between modes, resulting in a predicted value that differs significantly from the actual data. Consequently, to address this issue, a mixture distribution with parameters learned by a neural network, known as a Mixture Density Network (MDN), is typically employed. However, this approach has an important inherent limitation, in that it is not feasible to ascertain the precise number of components with a reasonable degree of accuracy. In this paper, we introduce CEC-MMR, a novel approach based on Cross-Entropy Clustering (CEC), which allows for the automatic detection of the number of components in a regression problem. Furthermore, given an attribute and its value, our method is capable of uniquely identifying it with the underlying component. The experimental results demonstrate that CEC-MMR yields superior outcomes compared to classical MDNs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression Byrski, Krzysztof Tabor, Jacek Spurek, Przemysław Mazur, Marcin Computer Vision and Pattern Recognition In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the univariate distribution, which is typically Gaussian, is suboptimal because the mean may be situated between modes, resulting in a predicted value that differs significantly from the actual data. Consequently, to address this issue, a mixture distribution with parameters learned by a neural network, known as a Mixture Density Network (MDN), is typically employed. However, this approach has an important inherent limitation, in that it is not feasible to ascertain the precise number of components with a reasonable degree of accuracy. In this paper, we introduce CEC-MMR, a novel approach based on Cross-Entropy Clustering (CEC), which allows for the automatic detection of the number of components in a regression problem. Furthermore, given an attribute and its value, our method is capable of uniquely identifying it with the underlying component. The experimental results demonstrate that CEC-MMR yields superior outcomes compared to classical MDNs. |
| title | CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.07301 |