A Stochastic Self-Organizing Map for Proximity Data

摘要

We derive an efficient algorithm for topographic mapping of proxim- ity data (TMP), which can be seen as an extension of Kohonen's self- organizing map to arbitrary distance measures. The TMP cost function is derived in a Baysian framework of folded Markov chains for the desctip- tion of autoencoders. It incorporates the data by a dissimilarity matrix D and the topographic neighborhood by a matrix H of transition proba- Bilities.