Data analysis, visualization, and hidden factor discovery by unsupervised learning
Erkki Oja, Kimmo Kiviluoto · 2003
With the continuous increase in computing power, it has become possible to process and classify masses of natural data, such as statistical information, images, speech, as well as other kinds of signals and measurements coming from very different sources. Many problems occur in industry, finance, remote sensing, medicine, and natural sciences, to mention only a few main fields, in which one needs efficient tools for visualization, prediction, clustering, and profiling. Often the explicit modelling of the processes underlying the measurements is very hard and so inferences from the measurement data must be made by learning methods. A widely used class of learning algorithms are the neural learning paradigms. In this paper, emphasis is on unsupervised neural learning. Especially the techniques of self-organizing maps and independent component analysis are reviewed and shown to be useful in this context. Some examples are shown on applications of these techniques on financial data analysis.