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Supervised Hessian Eigenmap for dimensionality reduction

Hessian Eigenmap is a proposed technique for dimensionality reduction. Many methods, such as ISOMAP, LLE, Laplacian Eigenmap, have been proposed under manifold learning for dimensionality reduction. However, all these ideas have not taken the influence of different class into consideration, which limit the effectiveness of manifold learning. To take account for the influence for multiclass and improve the performance of dimensional reduction, we propose a new method, supervised Hessian LLE(SHLLE). To evaluate the proposed method, extensive experiments are conducted on the artificial dataset and real dataset (COIL-20). Our result demonstrate that the proposed method outperform HLLE method.