Gene expression profiles have great potential for accurate tumor diagnosis. It is expected to enable us to diagnose tumors precisely and systematically, and also bring the researchers of machine learning two challenges, the curse of dimensionality and the small sample size problems. We propose a manifold learning based dimensional reduction algorithm named orthogonal local discriminant embedding (O-LDE) and apply it to tumor classification. Comparing with the classical local discriminant embedding (LDE), O-LDE aims to obtain an orthogonal linear projection matrix by solving an optimization problem. After being projected into a low-dimensional subspace by O-LDE, the data points of the same class maintain their intrinsic neighbor relations, whereas the neighboring points of the different classes are far from each other. Experimental results on a public tumor dataset validate the effectiveness and feasibility of the proposed algorithm.
Keywords: Tumor classification; local discriminant embedding (LDE); orthogonal local discriminant embedding (O-LDE).