Relative Transformation With CamNN Applied To Isometric Embedding

Neighborhood selection is one of the most important link in low-dimensional representations of high-dimensional data sets. Also, a good distance measure among the data points is where the shoe pinches.In this paper, we use the cam weighted distance to find a more flexible neighborhood of a data point in a newly-created space of r-isomap algorithm.It is a major advantage of r-isomap to optimize the process of intrinsic structure of the local information in a data set. Short-circuit edges are reduced in a certain extent because of the relative transformation space which is constructed in r-isomap. Furthermore, we can get a well performance on both orientation and scale adaptive side, because we utilize the cam weighted distance to search the neighborhood of a data point. It has been proved that this distance measure is more efficient than the Euclidean distance.Experiments demonstrated that the proposed method can give better results on dimension reduction than r-isomap, Weighted Locally Linear Embedding (WLLE) and some other approaches on the data sets which have obvious classifications. Especially robust to data sets with noise.