  In this paper, we propose a new deep learning network "GENet", it combines
the multi-layer network architec- ture and graph embedding framework. Firstly,
we use simplest unsupervised learning PCA/LDA as first layer to generate the
low- level feature. Secondly, many cascaded dimensionality reduction layers
based on graph embedding framework are applied to GENet. Finally, a linear SVM
classifier is used to classify dimension-reduced features. The experiments
indicate that higher classification accuracy can be obtained by this algorithm
on the CMU-PIE, ORL, Extended Yale B dataset.
