摘要 : We introduce stochastic proximity embedding (SPE) a novel self-organizing algorithm for producing meaningful underlying dimensions from proximity data SPE attempts to generate low-dimensional Euclidean embed-dings that best preser... 展开 We introduce stochastic proximity embedding (SPE) a novel self-organizing algorithm for producing meaningful underlying dimensions from proximity data SPE attempts to generate low-dimensional Euclidean embed-dings that best preserve the similarities between a set of related observations.The method starts with an initial configuration and iteratively refines it by repeatedly selecting pairs of objects at random and adjusting their coordinates so that their distances on the map match more closely their respective proximities The magnitude of these adjustments is controlled by a learning rate paramenter which decreases during the course of the simulation to avoid oscillatory behavior Unlike callssial multidimensional scaling (MDS)and nonliner mapping (NLM).SPE scales linearly with respect to sample size and can be applied to very large data sets that are intractable by conventianal embedding procedures The method is jprogrammatically simple robust and convergent and can be applied to a wide range of scientific problems involving exploratory data analysis and visulization. 收起