It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness.

翻译：在现实应用中，数据通常具有多个视图。充分探索每个视图的信息对于提升数据代表性具有重要意义。然而，由于数据采集和预处理过程中存在的各种局限与故障，真实数据不可避免地面临视图缺失和数据稀缺问题。这两个问题的并存使得模式分类任务的实现更具挑战性。据我们所知，当前鲜有方法能够同时有效处理这两个问题。为引起学界对此挑战的关注，本文提出一项新任务——少样本部分多视图学习，旨在克服低数据场景下视图缺失问题的负面影响。该任务面临双重挑战：（i）在缺失视图的干扰下，难以克服数据稀缺的影响；（ii）有限的数据量加剧了信息稀缺性，进而增加了解决视图缺失问题的难度。针对这些挑战，我们提出一种新的统一高斯稠密锚定方法。该方法为有限的局部多视图数据学习统一稠密锚点，将其锚定至统一稠密表示空间，从而缓解数据稀缺与视图缺失的影响。我们通过大量实验评估该方法，在Cub-googlenet-doc2vec、Handwritten、Caltech102、Scene15、Animal、ORL、tieredImagenet和Birds-200-2011数据集上的结果验证了其有效性。