Multiview learning has received substantial attention over the past decade due to its powerful capacity in integrating various types of information. Conventional unsupervised multiview dimension reduction (UMDR) methods are usually conducted in an offline manner and may fail in many real-world applications, where data arrive sequentially and the data distribution changes periodically. Moreover, satisfying the requirements of high memory consumption and expensive retraining of the time cost in large-scale scenarios are difficult. To remedy these drawbacks, we propose an online UMDR (OUMDR) framework. OUMDR aims to seek a low-dimensional and informative consensus representation for streaming multiview data. View-specific weights are also learned in this article to reflect the contributions of different views to the final consensus presentation. A specific model called OUMDR-E is developed by introducing the exclusive group LASSO (EG-LASSO) to explore the intraview and interview correlations. Then, we develop an efficient iterative algorithm with limited memory and time cost requirements for optimization, where the convergence of each update is theoretically guaranteed. We evaluate the proposed approach in video-based expression recognition applications. The experimental results demonstrate the superiority of our approach in terms of both effectiveness and efficiency.

中文翻译：

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流式多视图数据的高效无监督降维

多视图学习由于其强大的集成各种类型信息的能力，在过去十年中受到了广泛关注。传统的无监督多视图降维 (UMDR) 方法通常以离线方式进行，并且在许多实际应用中可能会失败，在这些应用中，数据按顺序到达并且数据分布会周期性变化。此外，难以满足大规模场景下高内存消耗和昂贵的时间成本再训练的要求。为了弥补这些缺点，我们提出了一个在线 UMDR (OUMDR) 框架。OUMDR 旨在为流式多视图数据寻求低维且信息丰富的共识表示。本文还学习了特定于视图的权重，以反映不同视图对最终共识呈现的贡献。通过引入独家组 LASSO (EG-LASSO) 来探索内部视图和访谈相关性，开发了一种称为 OUMDR-E 的特定模型。然后，我们开发了一种有效的迭代算法，具有有限的内存和时间成本要求进行优化，理论上保证了每次更新的收敛性。我们在基于视频的表情识别应用中评估所提出的方法。实验结果证明了我们的方法在有效性和效率方面的优越性。我们在基于视频的表情识别应用中评估所提出的方法。实验结果证明了我们的方法在有效性和效率方面的优越性。我们在基于视频的表情识别应用中评估所提出的方法。实验结果证明了我们的方法在有效性和效率方面的优越性。