Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for noisy, corrupted and out-of-distribution data. Dynamically assessing the trustworthiness of each view for different samples could provide reliable integration. This can be achieved through uncertainty estimation. With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed TMC can promote classification reliability by considering evidence from each view. Specifically, we introduce the variational Dirichlet to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
翻译:现有的多种观点分类算法侧重于通过利用不同观点提高准确性,通常将其纳入后续任务的共同表述中。虽然有效,但确保多观点整合和最终决定的可靠性也至关重要,特别是对于吵闹、腐败和分配外的数据。动态评估每种观点对不同样本的可靠性可以提供可靠的整合。这可以通过不确定性估计来实现。考虑到这一点,我们提议了一种新的多观点分类算法,称为可信赖的多观点分类(TMC),为通过动态整合不同观点在证据层面进行多观点学习提供了一个新的范例。拟议的TMC可以通过考虑每种观点的证据来提高分类可靠性。具体地说,我们采用变式分流模型来描述分类概率的分布情况,以不同观点的证据为参数,并与Dempster-Shafer理论相结合。统一学习框架带来了准确的不确定性,并因此使模型具有可靠性和可靠性,以防范可能的噪音或腐败。理论和实验结果都验证了拟议模型的准确性、稳健性和可靠性。