Multiview semi-supervised learning with consensus

• Main Authors: LI, Guangxia, CHANG, Kuiyu, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Artificial intelligence; learning systems; multiview learning; semi-supervised learning; support vector machines; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2283; https://ink.library.smu.edu.sg/context/sis_research/article/3283/viewcontent/Multiview_semi_supervised_learning_consensus_2012_afv.pdf
• Institution: Singapore Management University

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones. This paper demonstrates a way to improve the transductive SVM, which is an existing semi-supervised learning algorithm, by employing a multiview learning paradigm. Multiview learning is based on the fact that for some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such as term frequency, while a second view may contain a small but highly informative number of domain specific features. We propose a novel two-view transductive SVM that takes advantage of both the abundant amount of unlabeled data and their multiple representations to improve classification result. The idea is straightforward: train a classifier on each of the two views of both labeled and unlabeled data, and impose a global constraint requiring each classifier to assign the same class label to each labeled and unlabeled sample. We also incorporate manifold regularization, a kind of graph-based semi-supervised learning method into our framework. The proposed two-view transductive SVM was evaluated on both synthetic and real-life data sets. Experimental results show that our algorithm performs up to 10 percent better than a single-view learning approach, especially when the amount of labeled data is small. The other advantage of our two-view semi-supervised learning approach is its significantly improved stability, which is especially useful when dealing with noisy data in real-world applications.