Global Inference Preserving Projection for Semi-supervised Discriminant Analysis

Semi-supervised dimensionality reduction is an important research area for data classification. A new linear dimensionality reduction approach, global inference preserving projection （GIPP）, was proposed to perform classification task in semi-supervised case. GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples. It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the diseriminant algorithm into a generalized eigenequation problem. Experimental results demonstrate the effectiveness of the proposed approach.

Semi-supervised dimensionality reduction is an important research area for data classification.A new linear dimensionality reduction approach,global inference preserving projection(GIPP),was proposed to perform classification task in semi-supervised case.GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples.It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the discriminant algorithm into a generalized eigenequation problem.Experimental results demonstrate the effectiveness of the proposed approach.