A graph based subspace semi-supervised learning framework for dimensionality reduction

Abstract

The key to the graph based semi-supervised learning algorithms for classification problems is how to construct the weight matrix of the p-nearest neighbor graph. A new method to construct the weight matrix is proposed and a graph based Subspace Semi-supervised Learning Framework (SSLF) is developed. The Framework aims to find an embedding transformation which respects the discriminant structure inferred from the labeled data, as well as the intrinsic geometrical structure inferred from both the labeled and unlabeled data. By utilizing this framework as a tool, we drive three semi-supervised dimensionality reduction algorithms: Subspace Semi-supervised Linear Discriminant Analysis (SSLDA), Subspace Semi-supervised Locality Preserving Projection (SSLPP), and Subspace Semi-supervised Marginal Fisher Analysis (SSMFA). The experimental results on face recognition demonstrate our subspace semi-supervised algorithms are able to use unlabeled samples effectively. © 2008 Springer Berlin Heidelberg.