Dimensionality reduction with the k-associated optimal graph applied to image classification

In this paper, we aim to study the usage of different network formation methods into a graph embedding framework to perform supervised dimensionality reduction. Images are often high-dimensional patterns, and dimensionality reduction can enhance processing and also increase classification accuracy. Specifically, our technique maps images into networks and constructs two network adjacency matrices to convey information about intra-class components and inter-class penalty connections. Both matrices are inserted into an optimization framework in order to achieve a projection vector that is used to project high-dimension data samples into a low-dimensional space. One advantage of the technique is that no parameter is required, that is, there is no need to select a model for the input data. Applications on handwritten digits recognition are performed, and the proposed technique is compared to some classical network formation methods. Numerical results show the approach is promising.