Supervised Discriminant Projection for Feature Extraction

Two-Dimensional Supervised Discriminant Projection Method For Feature Extraction

For supervised discriminant projection (SDP)method, the image matrix data are vectorized to find the intrinsic manifold structure, and the dimension of matrix data is usually very high, so SDP cannot be performed because of the singularity of scatter matrix. In addition, the matrix-to-vector transform procedure may cause the loss of some useful structural information embedding in the original i...

Supervised feature extraction based on orthogonal discriminant projection

In this paper, a supervised feature extraction method, named orthogonal discriminant projection (ODP), is presented. As an extension of spectral mapping method, the proposed algorithm maximizes the weighted difference between the non-local scatter and the local scatter. Moreover, the weights between two nodes of a graph are adjusted according to their class information and local information. Ex...

Unsupervised Discriminant Projection Analysis for Feature Extraction

This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method ocality preserving projection (LP...

Feature Extraction with Supervised Projection Pursuit

Supervised Methods for Feature Extraction

It is present in this paper the feature extraction for pattern recognition tasks. It is proposed two approaches. In the first, it is used weights to scale the coordinates of the features vector in order to increase the precision of statistical classifiers. Genetic algorithm is intended to do weight adjustments. In the second approach the Battacharyya metric is suggested. Theses approaches make ...