CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression
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Abstract (arXiv)
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.
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References · 14
- 2018Neural Ordinary Differential Equationscited by 3,779
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
- 2016Stochastic Multiple Choice Learning for Training Diverse Deep Ensemblescited by 77
- 2018Flow-Grounded Spatial-Temporal Video Prediction from Still Imagescited by 55
- 2018Conditional Density Estimation with Bayesian Normalising Flowscited by 54
- 2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
- 2019Conditional Density Estimation with Neural Networks: Best Practices and Benchmarkscited by 38
- 2019Noise Regularization for Conditional Density Estimationcited by 23
- 2018Bayesian Prediction of Future Street Scenes using Synthetic Likelihoodscited by 17
- 2017The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variablescited by 15
- 2020An implicit function learning approach for parametric modal regressioncited by 7
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- 2015Active Function Cross-Entropy Clusteringcited by 3
- 2019How do Mixture Density RNNs Predict the Future?cited by 2
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