Prototype vector machine for large scale semi-supervised learning

Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or graph regularization, which in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of "prototypes vectors" for efficient approximation on both the graphbased regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number ...

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Kai Zhang, James T. Kwok, Bahram Parvin
• Keywords: Effective Low-rank Approximation, Graph-based Ssl Arises, ICML 2009, Large-scale Semi-supervised Learning, Machine Learning