利用偏最小二乘方法修复高光谱影像等距映射降维中遗失点的坐标

Manifold Coordinate Repairing of Lost Points with PLS for Isomap Nonlinear Dimensionality Reduction of Hyperspectral Image

摘要: 将Isomap流形学习方法应用于高光谱影像非线性降维时,在构建最短路径过程中,其边界点往往被忽略而没有低维流形坐标。对此,引入偏最小二乘方法来模拟修复遗失点的流形坐标,并从两个方面进行了综合评价。实验结果表明,模拟流形坐标与实际坐标吻合很好。

Abstract: As a manifold learning method,Isomap has been widely used for making nonlinearly reduction for hyperspectral image.However,during the construction process of the shortest path graph,the boundary points,which are not noise points,have always been omitted for the consideration of the stability of the graph.Therefore,the PLS method is introduced to repair and simulate the manifold coordinates of the lost points in the shortest path graph.And the simulated manifold coordinates have been evaluated from two different aspects to verify our method.The results show that the simulated manifold coordinates agree well with the real one.It will be quite useful for further classification or visualization with low dimensional manifold image.