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，数据往往具有多个视图。充分探索每个视图的信息对于提升数据的代表性具有重要意义。然而，由于数据采集和预处理过程中的各种限制与失败，真实数据不可避免地会面临视图缺失和数据稀缺的问题。这两个问题的共存使得模式分类任务更具挑战性。当前，据我们所知，鲜有合适的方法能够同时妥善处理这两个问题。为引起学界对这一挑战的关注，本文提出一项新任务——小样本部分多视图学习，其核心目标是在低数据量场景下克服视图缺失问题带来的负面影响。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，进而使得解决视图缺失问题更加困难。为应对这些挑战，我们提出一种新的统一高斯密集锚定方法。通过学习有限部分多视图数据的统一密集锚点，将其锚定到统一的密集表示空间中，从而缓解数据稀缺和视图缺失的影响。我们开展了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。