CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression

2025-04-09

AI Summary

In multimodal regression, conventional unimodal assumptions often lead to systematic prediction bias when target distributions exhibit multiple modes. To address this, we propose CEC-MMR—a novel end-to-end multimodal regression framework grounded in Cross-Entropy Clustering (CEC). Unlike traditional Mixture Density Networks (MDNs), which require manual specification of the number of mixture components and lack inherent modality identifiability, CEC-MMR is the first to incorporate unsupervised CEC into regression modeling, automatically estimating the optimal number of modes without prior knowledge. Furthermore, it employs joint neural network optimization to achieve interpretable assignment of predicted attribute values to their corresponding modal components. Evaluated on multiple benchmark datasets, CEC-MMR consistently outperforms MDN baselines in both prediction accuracy and modality identification fidelity. It offers superior generalization capability and intrinsic interpretability, bridging a critical gap between expressive modeling and semantic transparency in multimodal regression.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Addresses multi-modal regression with clustered attribute values
• Automatically detects optimal number of mixture components
• Improves accuracy over traditional Mixture Density Networks

Innovation

• Uses Cross-Entropy Clustering for multi-modal regression
• Automatically detects number of mixture components
• Identifies unique components for given attribute values