"Non-linear dimensionality reduction techniques for classification and visualization," Vlachos, et al., KDD 2002.

In this paper, they use non-linear dimensionality reduction techniques to address two problem. One is visualization of high dimensional data, another is curse-of dimensionality problem of classification. In problem one, it propose that LLE and Isomap can not preform well when data are sample from different clusters. They improve the solution by keep distance to both nearest and farthest object when mapping. They solve the second problem by introduce two techniques, weightedIso and Iso+Ada which modifying the transformation performed by the Isomap and produce homogeneous neighborhoods in reduced space. The experiment shows that it perform as well as other method but in a much lower dimensional.