Neural networks: a systematic introduction
Neural networks: a systematic introduction
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Information Dynamics: Foundations and Applications
Information Dynamics: Foundations and Applications
Self-Organizing Maps
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
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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.