Neighborhood Function Design for Embedding in Reduced Dimension

LLE(Local linear embedding) is a widely used approach for dimension reduction. The neighborhood selection is an important issue for LLE. In this paper, the ε-distance approach and a slightly modified version of k-nn method are introduced. For different types of datasets, different approaches are needed in order to enjoy higher chance to obtain better representation. For some datasets with complex structure, the proposed ε-distance approach can obtain better representations. Different neighborhood selection approaches will be compared by applying them to different kinds of datasets.