Supervised local spline embedding for medical diagnosis

Abstract

A common difficulty of intelligent medical diagnosis is the high dimensionality of medical data. Manifold learning provides an elegant way to solve this problem by mapping the high-dimensional data into the low-dimensional embedding. However, traditional manifold learning algorithms fail to fully utilize the supervised information in medical diagnosis. To overcome this problem, in this paper we propose a novel Supervised Local Spline Embedding (SLSE) algorithm, which incorporates the supervised information into the local spline manifold embedding. SLSE not only preserves the local neighborhood structure, but also utilizes the global manifold shape through spline interpolation. Moreover, SLSE leverages the supervised information by maximizing the inter-class scatterness and minimizing the intra-class scatterness in the low-dimensional embedding. The promising experimental results on real-world medical datasets illustrate the superiority of our proposed approach in comparison with the existing popular manifold learning algorithms.