Two-View Transductive Support Vector Machines

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam detection, which changes at a very brisk pace. 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 thus propose a novel two-view transductive SVM that takes advantage of both the abundant amount of unlabeled data and their multiple representations to improve the performance of classifiers. The idea is fairly simple: train a classifier on each of the two views of both labeled and unlabeled data, and impose a global constraint that each classifier assigns the same class label to each labeled and unlabeled data. We applied our two-view transduc...

• Type: Conference
• Year: 2010
• Where: SDM
• Authors: Guangxia Li, Steven C. H. Hoi, Kuiyu Chang
• Keywords: Data Mining, SDM 2010, Two-view Transductive Svm, Unlabeled Data, Up-to-date Labeled Data