Research

Multi-Label Classificaiton
Unlike traditional single-label classification where each instance is associated with only one label, Multi-Label Classification (MLC) refers to the problems assigning multiple labels to a single test instance. MLC can be seen in a wide range of real-world applications such as text categorization, semantic image classification, bioinformatics analysis and video annotation. In fact, MLC is ubiquitous in real-world problems. For example, a news article is possibly relevant to multiple topics, like “science”, “technology”, “economics”, “politics”, etc; a single image is probably associated with a set of semantic concepts, like “sky”, “sea”, “field”, “building”, etc.

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Multi-View Multi-Task Learning
Multi-View Multi-Task Learning (MVMTL) is an important and promising direction of Machine Learning, due to its flexibility in data representation and ubiquity in modern real-world applications. In MVMTL, each sample are represented by several feature sets collected from a variety of data sources/views, while targets/tasks are correlated with each other. A number of traditional Machine Learning problems and recently emerging real-world applications can be considered as MVMTL problems. For example, in web page classification, each web page has three feature sets (views): title, image and text, and possibly relates with multiple functional classes (tasks), like space explorer, JAXA and Ryugu, etc. To utilize the information among tasks and views, MVMTL learns related tasks together (task correlation) by fusing feature sets from different views (view consistency).

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