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  1. Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, including multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. We further present more practicalmore »challenges in multi-view data analysis. Finally, we discuss future research including incomplete, unbalance, large-scale multi-view learning. This would benefit AI community from literature review to future direction.

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  2. Multi-label annotation is challenging since a large amount of well-labeled training data are required to achieve promising performance. However, providing such data is expensive while unlabeled data are widely available. To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale unlabeled data to facilitate learning performance. Specifically, a multi-label propagation scheme and an effective embedding are jointly learned to seek a latent space where unlabeled instances tend to be well assigned multiple labels. Furthermore, a locality structure regularizer is designed to preservemore »the intrinsic structure and enhance the multi-label annotation. We evaluate our model in both conventional multi-label learning and zero-shot learning scenario. Experimental results demonstrate that our approach outperforms other compared state-of-the-art methods.

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