Visualizing Multidimensional Data through Multilayer Perceptron Maps

Visualization of high-dimensional data is a major task in data mining. The main idea of visualization is to map data from the highdimensional space onto a certain position in a low-dimensional space. From all mappings, only those that lead to maps that are good approximations of the data distribution observed in the high-dimensional space are of interest. Here, we present a mapping scheme based on multilayer perceptrons that forms a two-dimensional representation of highdimensional data. The core idea is that the system maps all vectors to a certain position in the two-dimensional space. We then measure how much does this map resemble the distribution in the original highdimensional space, which leads to an error measure. Based on this error, we apply reinforcement learning to multilayer perceptrons to find good maps. We present here the description of the model as well as some results in well-known benchmarks. We conclude that the multilayer perceptron is a good tool to visualize high-dimensional data.