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With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named multi-view semi-supervised classification via adaptive...
In multi-task learning, it is paramount to discover the relational structure of tasks and utilize the learned task structure. Previous works have been using the low-rank latent feature subspace to capture the task relations, and some of them aim to learn the group based relational structure of tasks. However, in many cases, the low-rank subspace may not exist for the specific group of tasks, thus...
Recent multimedia research has increasingly focused on large scale multimodal data from disparate geospatial sensors. In addition to the volume of the data, the diversity and granularity of the data poses a major challenge in extracting meaningful and actionable information. To address this, we present a novel spatial interpolation framework, capable of incorporating multimodal data sources and modeling...
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