Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures

TL;DR

This paper introduces KPHD-Net, a novel multi-view learning framework that uses H"older divergence and Dempster-Shafer theory to improve uncertainty estimation, robustness, and reliability in classification and clustering tasks.

Contribution

It proposes KPHD-Net, integrating H"older divergence with Dempster-Shafer evidence theory and Kalman filtering for enhanced uncertainty quantification in multi-view learning.

Findings

Outperforms state-of-the-art methods in accuracy.

Provides more reliable uncertainty estimates.

Demonstrates robustness to noisy data.

Abstract

Existing multi-view classification and clustering methods typically improve task accuracy by leveraging and fusing information from different views. However, ensuring the reliability of multi-view integration and final decisions is crucial, particularly when dealing with noisy or corrupted data. Current methods often rely on Kullback-Leibler (KL) divergence to estimate uncertainty of network predictions, ignoring domain gaps between different modalities. To address this issue, KPHD-Net, based on H\"older divergence, is proposed for multi-view classification and clustering tasks. Generally, our KPHD-Net employs a variational Dirichlet distribution to represent class probability distributions, models evidences from different views, and then integrates it with Dempster-Shafer evidence theory (DST) to improve uncertainty estimation effects. Our theoretical analysis demonstrates that Proper H\"older divergence offers a more effective measure of distribution discrepancies, ensuring enhanced performance in multi-view learning. Moreover, Dempster-Shafer evidence theory, recognized for its superior performance in multi-view fusion tasks, is introduced and combined with the Kalman filter to provide future state estimations. This integration further enhances the reliability of the final fusion results. Extensive experiments show that the proposed KPHD-Net outperforms the current state-of-the-art methods in both classification and clustering tasks regarding accuracy, robustness, and reliability, with theoretical guarantees.